Transcript DDE 63

• Data
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Data Interchange Format Discrepancies in implementations
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Some implementations (notably those of
older Microsoft products) swapped the
meaning of VECTORS and TUPLES.
Some implementations are insensitive to
errors in the dimensions of the table as
written in the header and simply use the
layout in the DATA section.
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Data Interchange Format History
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DIF was developed by Software Arts, Inc.
(the developers of the VisiCalc program)
in the early 1980s. The specification was
included in many copies of VisiCalc, and
published in Byte Magazine. Bob
Frankston developed the format, with
input from others, including Mitch Kapor,
who helped so that it could work with his
VisiPlot program. (Mitch later went on to
found Lotus and make Lotus 1-2-3
happen.) The specification was copyright
1981.
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Data Interchange Format History
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DIF was a registered trademark of
Software Arts Products Corp. (a legal
name for Software Arts at the time).
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Data Interchange Format Syntax
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On the other hand, data chunks start
with a number pair and the next line is
a quoted string or a keyword.
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Data Interchange Format Values
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A value occupies two lines, the first a pair
of numbers and the second either a string
or a keyword. The first number of the pair
indicates type:
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Data Interchange Format Values
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BOT – beginning of
tuple (start of row)
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Data Interchange Format Values
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0 – numeric type, value is the second number,
the following line is one of these keywords:
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Data Interchange Format
Data Interchange Format (.dif) is a
text file format used to import/export
single spreadsheets between
spreadsheet programs
(OpenOffice.org Calc, Excel,
Gnumeric, StarCalc, Lotus 1-2-3,
FileMaker, dBase, Framework,
Multiplan, etc.). It is also known as
"Navy DIF". One limitation is that DIF
format cannot handle multiple
spreadsheets in a single workbook.
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Data Interchange Format Header chunk
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A header chunk is composed of an identifier line
followed by the two lines of a value.
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Data Interchange Format Header chunk
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TABLE - a numeric value follows of the
version, the disused second line of the
value contains a generator comment
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Data Interchange Format Header chunk
VECTORS - the number of
columns follows as a numeric
value
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Data Interchange Format Header chunk
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TUPLES - the number of
rows follows as a
numeric value
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Data Interchange Format Header chunk
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DATA - after a dummy 0 numeric value,
the data for the table follow, each row
preceded by a BOT value, the entire table
terminated by an EOD value
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Data Interchange Format Header chunk
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The numeric values in header chunks use just an
empty string instead of the validity keywords.
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Comparison of spreadsheet software Remote data update
Some on-line spreadsheets provide
remote data update allowing data
values to be extracted from other users'
spreadsheets even though they may be
inactive at the time.
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Comparison of spreadsheet software Remote data update
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Gnumeric (listed below as a desktop
spreadsheet) is also used as the
"back-end" processor in at least one
on-line spreadsheet application
(Editgrid).
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Computer file Organizing the data in a file
Information in a computer file can
consist of smaller packets of
information (often called "records" or
"lines") that are individually different
but share some common traits
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Computer file Organizing the data in a file
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The way information is grouped into a
file is entirely up to how it is designed.
This has led to a plethora of more or less
standardized file structures for all
imaginable purposes, from the simplest
to the most complex. Most computer
files are used by computer programs
which create, modify or delete the files
for their own use on an as-needed basis.
The programmers who create the
programs decide what files are needed,
how they are to be used and (often) their
names.
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Computer file Organizing the data in a file
In some cases, computer programs
manipulate files that are made visible to the
computer user. For example, in a wordprocessing program, the user manipulates
document files that the user personally
names. Although the content of the document
file is arranged in a format that the wordprocessing program understands, the user is
able to choose the name and location of the
file and provide the bulk of the information
(such as words and text) that will be stored in
the file.
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Computer file Organizing the data in a file
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Many applications pack all their data files
into a single file called archive file, using
internal markers to discern the different
types of information contained within. The
benefits of the archive file are to lower the
number of files for easier transfer, to
reduce storage usage, or just to organize
outdated files. The archive file must often
be unpacked before next using.
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Microsoft Excel Using external data
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Excel users can access external data
sources via Microsoft Office features
such as (for example) .odc
connections built with the Office Data
Connection file format. Excel files
themselves may be updated using a
Microsoft supplied ODBC driver.
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Microsoft Excel Using external data
Excel can accept data in real time
through several programming
interfaces, which allow it to
communicate with many data sources
such as Bloomberg and Reuters
(through addins such as Power Plus
Pro).
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Microsoft Excel Using external data
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DDE : "Dynamic Data Exchange" uses the
message passing mechanism in Windows
to allow data to flow between Excel and
other applications. Although it is easy for
users to create such links, programming
such links reliably is so difficult that
Microsoft, the creators of the system,
officially refer to it as "the protocol from
hell". In spite of its many issues DDE
remains the most common way for data to
reach traders in financial markets.
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Microsoft Excel Using external data
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Network DDE Extended the protocol to
allow spreadsheets on different
computers to exchange data. Given
the view above, it is not surprising
that in Vista, Microsoft no longer
supports the facility.
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Microsoft Excel Using external data
Real Time Data : RTD although in
many ways technically superior to
DDE, has been slow to gain
acceptance, since it requires nontrivial programming skills, and when
first released was neither adequately
documented nor supported by the
major data vendors.
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Microsoft Excel Using external data
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Alternatively, Microsoft Query provides ODBC-based
browsing within Microsoft Excel.
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Micah Altman Data curation
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To yield reliable and comparable results,
standard methods of data encoding are
needed for data attribution and data
citation, and for maximally accurate data
verification and replication.
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Micah Altman Data curation
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The citation information they recommended
included a unique global identifier, a short
character string guaranteed to be unique
among all such identifiers, that permanently
identifies the data set independent of its
location, and a universal numeric fingerprint,
a fixed-length string of numbers and
characters that summarize all the content in
the data set, such that a change in any part
of the data would produce a different
fingerprint.
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Micah Altman Data curation
The algorithm for generating the
fingerprints has undergone several
revisions because the initial versions
underestimated the expressive power
needed to encode the data and the
simpler algorithm inherited the
weaknesses of the MD5 hash function that
was shown to have several vulnerabilities.
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Data acquisition
Data acquisition is the process of
sampling signals that measure real
world physical conditions and
converting the resulting samples into
digital numeric values that can be
manipulated by a computer. Data
acquisition systems (abbreviated with
the acronym DAS or DAQ) typically
convert analog waveforms into digital
values for processing. The components
of data acquisition systems include:
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Data acquisition
Signal conditioning circuitry to
convert sensor signals into a form that
can be converted to digital values.
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Data acquisition
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Analog-to-digital converters, which convert
conditioned sensor signals to digital values.
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Data acquisition
Data acquisition applications are
controlled by software programs
developed using various general purpose
programming languages such as BASIC,
C, Fortran, Java, Lisp, Pascal.
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Data acquisition History
In 1963, IBM produced computers which
specialized in data acquisition. These include
the IBM 7700 Data Acquisition System and its
successor, the IBM 1800 Data Acquisition
and Control System. These expensive
specialized systems were surpassed in 1974
by general purpose S-100 computers and
data acquisitions cards produced by
Tecmar/Scientific Solutions Inc. In 1981 IBM
introduced the IBM Personal Computer and
Scientific Solutions introduced the first PC
data acquisition products.
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Data acquisition Source
Data acquisition begins with the physical
phenomenon or physical property to be
measured. Examples of this include
temperature, light intensity, gas pressure,
fluid flow, and force. Regardless of the type of
physical property to be measured, the
physical state that is to be measured must
first be transformed into a unified form that
can be sampled by a data acquisition system.
The task of performing such transformations
falls on devices called sensors.
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Data acquisition Source
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A sensor, which is a type of transducer,
is a device that converts a physical
property into a corresponding electrical
signal (e.g., Strain gauge, thermistor)
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Data acquisition DAQ hardware
DAQ hardware is
what usually
interfaces between
the signal and a PC
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Data acquisition DAQ hardware
DAQ cards often contain multiple components
(multiplexer, ADC, DAC, TTL-IO, high speed timers,
RAM)
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Data acquisition DAQ software
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DAQ software is needed in order for
the DAQ hardware to work with a PC.
The device driver performs low-level
register writes and reads on the
hardware, while exposing a standard
API for developing user applications.
A standard API such as COMEDI
allows the same user applications to
run on different operating systems,
e.g. a user application that runs on
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Data acquisition Hardware
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LAN eXtensions for
Instrumentation
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Data acquisition Hardware
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PCI eXtensions for
Instrumentation
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Data acquisition Software
Specialized software tools used for
building large-scale data acquisition
systems include EPICS. Graphical
programming environments include ladder
logic, Visual C++, Visual Basic, and
LabVIEW. :
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Data acquisition Further reading
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Simon S. Young (2001). Computerized
Data Acquisition and Analysis for the
Life Sciences. Cambridge University
Press. ISBN 0-521-56570-7.
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Data acquisition Further reading
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W. R. Leo (1994). Techniques for Nuclear
and Particle Physics Experiments.
Springer. ISBN 3-540-57280-5.
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Data acquisition Further reading
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Charles D. Spencer (1990). Digital Design
for Computer Data Acquisition. Cambridge
University Press. ISBN 0-521-37199-6.
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Data acquisition Further reading
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B.G. Thompson & A. F. Kuckes (1989).
IBM-PC in the laboratory. Cambridge
University Press. ISBN 0-521-32199-9.
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Microsoft Office 2007 Metadata
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In Office 2007, Microsoft introduced the
Document Inspector, an integral
metadata removal tool which strips
Word, Excel, and PowerPoint
documents of information such as
author name and comments and other
"metadata".
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C (programming language) Data types
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C has a static weak typing type system
that shares some similarities with that
of other ALGOL descendants such as
Pascal. There are built-in types for
integers of various sizes, both signed
and unsigned, floating-point numbers,
characters, and enumerated types
(enum). C99 added a boolean datatype.
There are also derived types including
arrays, , records (struct), and
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C (programming language) Data types
C is often used in low-level systems
programming where escapes from the
type system may be necessary. The
compiler attempts to ensure type
correctness of most expressions, but the
programmer can override the checks in
various ways, either by using a type cast
to explicitly convert a value from one type
to another, or by using or unions to
reinterpret the underlying bits of a data
object in some other way.
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C (programming language) Data types
Some find C's declaration syntax
unintuitive, particularly for function .
(Ritchie's idea was to declare
identifiers in contexts resembling
their use: "declaration reflects use".)
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C (programming language) Data types
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C's usual arithmetic conversions allow for
efficient code to be generated, but can
sometimes produce unexpected results.
For example, a comparison of signed and
unsigned integers of equal width requires
a conversion of the signed value to
unsigned. This can generate unexpected
results if the signed value is negative.
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Dynamic Data Exchange
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In computing, Dynamic Data Exchange
(DDE) is a method of interprocess
communication under Microsoft Windows
or OS/2. It allows one program to
subscribe to items made available by
another program, for example a cell in a
Microsoft Excel spreadsheet, and be
notified whenever a change of that item
occurs. That method was partially
superseded by Object Linking and
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Dynamic Data Exchange Overview
Dynamic Data Exchange was first
introduced in 1987 with the release of
Windows 2.0 as a method of
interprocess communication so that
one program can communicate with or
control another program, somewhat
like Sun's RPC (Remote Procedure Call)
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Dynamic Data Exchange Overview
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Each application could further organize
information by groups known as "topic"
and each topic could serve up individual
pieces of data as an "item"
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Dynamic Data Exchange Overview
DDE has also been widely used in the
SAS programming language for
manipulating Excel and transferring data
between SAS and Excel and can be used
to format Excel workbooks from within a
SAS program.
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Dynamic Data Exchange Overview
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For example, a single DDE financial data
distribution application can provide live
prices to either Excel or a financial
charting application without needing to
know which it is doing, while to achieve
the same results with COM would usually
require the distribution application's
authors to write custom code for each use
scenario.
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Dynamic Data Exchange NetDDE
California-based company
Wonderware developed an extension
for DDE called NetDDE that could be
used to initiate and maintain the
network connections needed for DDE
conversations between DDE-aware
applications running on different
computers in a network and
transparently exchange data. A DDE
conversation is an interaction between
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Dynamic Data Exchange NetDDE
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NETDDE.EXE (Network
DDE - DDE
Communication)
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Dynamic Data Exchange NetDDE
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Microsoft licensed a basic (NetBIOS
Frames protocol only) version of the
product for inclusion in various
versions of Windows from Windows for
Workgroups to Windows XP. In
addition, Wonderware also sold an
enhanced version of NetDDE to their
own customers that included support
for TCP/IP. The technology is
extensively used in the SCADA field.
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Dynamic Data Exchange NetDDE
NetDDE was still included with
Windows Server 2003 and Windows XP
Service Pack 2, although it was
disabled by default. It has been
removed entirely in Windows Vista.
However, this will not prevent existing
versions of NetDDE from being
installed and functioning on later
versions of Windows.
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Explicit Congestion Notification Data Center TCP
Data Center TCP (DCTCP) utilizes
ECN to enhance the Transmission
Control Protocol congestion control
algorithm. It is used in data center
networks. Whereas the standard TCP
congestion control algorithm is only
able to detect the presence of
congestion, DCTCP, using ECN, is
able to gauge the extent of congestion.
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Converged infrastructure The evolution of data centers
Historically, to keep pace with the
growth of business applications and
the terabytes of data they generate, IT
resources were deployed in a silo-like
fashion. One set of resources has been
devoted to one particular computing
technology, business application or
line of business. These resources
support a single set of assumptions
and cannot be optimized or
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Converged infrastructure The evolution of data centers
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The proliferation of IT sprawl in data
centers has contributed to rising
operations costs, reducing productivity,
and stifling agility and flexibility
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Converged infrastructure The evolution of data centers
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A converged infrastructure addresses the
problem of siloed architectures and IT
sprawl by pooling and sharing IT
resources. Rather than dedicating a set of
resources to a particular computing
technology, application or line of business,
converged infrastructures creates a pool of
virtualized server, storage and networking
capacity that is shared by multiple
applications and lines of business.
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Common Lisp Data types
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Common Lisp has
many data types—
more than many
other languages.
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Common Lisp Data structures
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Sequence types in Common Lisp include
lists, vectors, bit-vectors, and strings.
There are many operations which can
work on any sequence type.
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Common Lisp Data structures
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It is also possible to create
circular data structures with
conses.
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Common Lisp Data structures
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Common Lisp supports multidimensional arrays, and
can dynamically resize arrays if required
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Common Lisp Data structures
Hash tables store associations
between data objects. Any object may
be used as key or value. Hash tables,
like arrays, are automatically resized
as needed.
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Common Lisp Data structures
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Packages are collections of symbols,
used chiefly to separate the parts of a
program into namespaces. A package
may export some symbols, marking
them as part of a public interface.
Packages can use other packages.
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Common Lisp Data structures
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Structures, similar in use to C structs
and Pascal records, represent arbitrary
complex data structures with any
number and type of fields (called slots).
Structures allow single-inheritance.
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Common Lisp Data structures
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Classes are similar to structures, but
offer more dynamic features and
multiple-inheritance. (See CLOS).
Classes have been added late to
Common Lisp and there is some
conceptual overlap with structures.
Objects created of classes are called
Instances. A special case are Generic .
Generic are both and instances.
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Census Uses of Census data
In the nineteenth century, the first
censuses collected paper
enumerations that had to be collated by
hand so the statistical uses were very
basic. The government owned the data
and were able to publish statistics
themselves on the state of the nation.
Uses were to measure changes in the
population and apportion
representation. Population estimates
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Census Uses of Census data
By the beginning of the twentieth
century, censuses were recording
households and some indications of their
employment. In some countries, census
archives are released for public
examination after many decades,
allowing genealogists to track the
ancestry of interested people. Archives
provide a substantial historical record
which may challenge established notions
of tradition. It is also possible to
understand the societal history through
job titles and arrangements for the
destitute and sick.
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Census Uses of Census data
As governments assumed
responsibility for schooling and
welfare, large government
departments made extensive use of
census data. Actuarial estimates could
be made to project populations and
plan for provision in local government
and regions. It was also possible for
central government to allocate
funding on the basis of census data.
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Census Uses of Census data
Even into the mid twentieth century,
census data was only directly accessible
to large government departments.
However, computers meant that
tabulations could be used directly by
university researchers, large businesses
and local government offices. They could
use the detail of the data to answer new
questions and add to local and specialist
knowledge.
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Census Uses of Census data
1
Census data offer a unique insight into
small areas and small demographic
groups which sample data would be
unable to capture with precision.
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Burroughs large systems DMALGOL and databases
Normal user-level programs obtain
database access by using code written in
application languages, mainly ALGOL and
COBOL, extended with database
instructions and transaction processing
directives
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Burroughs large systems DMALGOL and databases
DMALGOL preprocessing includes
variables and loops, and can generate
names based on compile-time
variables. This enables tailoring far
beyond what can be done by
preprocessing facilities which lack
loops.
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Burroughs large systems DMALGOL and databases
This means that, unlike in other DBMS
implementations, there is often no need for
database-specific if/then/else code at runtime
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Burroughs large systems DMALGOL and databases
1
Roy Guck of Burroughs
was one of the main
developers of DMSII.
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Burroughs large systems DMALGOL and databases
In later years, with compiler code size
being less of a concern, most of the
preprocessing constructs were made
available in the user level of ALGOL. Only
the unsafe constructs and the direct
processing of the database description file
remain restricted to DMALGOL.
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Data buffer
1
Buffers are typically used when there is a
difference between the rate at which data
is received and the rate at which it can be
processed, or in the case that these rates
are variable, for example in a printer
spooler or in online video streaming.
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Data buffer
A buffer often adjusts timing by
implementing a queue (or FIFO) algorithm
in memory, simultaneously writing data
into the queue at one rate and reading it at
another rate.
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Data buffer Applications
1
Buffers are usually used in a FIFO (first in,
first out) method, outputting data in the
order it arrived.
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Data buffer Telecommunication buffer
1
A buffer routine or storage medium used in
telecommunications compensates for a
difference in rate of flow of data, or time of
occurrence of events, when transferring
data from one device to another.
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Data buffer Telecommunication buffer
1
Buffers are used for many
purposes, including:
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Data buffer Telecommunication buffer
Interconnecting two digital
circuits operating at different
rates,
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Data buffer Telecommunication buffer
1
Holding data for later use,
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Data buffer Telecommunication buffer
1
Collecting binary data bits into groups that can
then be operated on as a unit,
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Data buffer Telecommunication buffer
1
Delaying the transit time of a signal in order to allow
other operations to occur.
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Data buffer Buffer versus cache
1
A cache often also acts as a buffer, and
vice versa. However, cache operates
on the premise that the same data will
be read from it multiple times, that
written data will soon be read, or that
there is a good chance of multiple
reads or writes to combine to form a
single larger block. Its sole purpose is
to reduce accesses to the underlying
slower storage. Cache is also usually
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Data buffer Buffer versus cache
A "Disk Cache" or "File Cache" keeps
statistics on the data contained within it
and commits data within a time-out period
in write-back modes. A buffer does none of
this.
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Data buffer Buffer versus cache
1
A buffer is primarily used for input, output,
and sometimes very temporary storage of
data that is either en route between other
media or data that may be modified in a
non-sequential manner before it is written
(or read) in a sequential manner.
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Data buffer Buffer versus cache
1
Good examples include:
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Data buffer Buffer versus cache
The BUFFERS
command/statement in
CONFIG.SYS of DOS.
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Data buffer Buffer versus cache
1
The buffer between a serial port (UART)
and a MODEM. The COM port speed may
be 38400 bit/s while the MODEM may only
have a 14400 bit/s carrier.
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Data buffer Buffer versus cache
1
The integrated buffer on a Hard Disk Drive,
Printer or other piece of hardware.
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Data buffer Buffer versus cache
1
The Framebuffer on a
video card.
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Data buffer History
1
An early mention of a print buffer is the
Outscriber devised by image processing
pioneer Russel A. Kirsch for the SEAC
computer in 1952
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Data buffer History
1
Thus, better efficiency is achieved in
recording the output data; transcription
can be made later from the magnetic
recording device to a printing device
without tying up the main computer.
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Batch processing Databases
1
Batch processing is also used for efficient
bulk database updates and automated
transaction processing, as contrasted to
interactive online transaction processing
(OLTP) applications. The extract,
transform, load (ETL) step in populating
data warehouses is inherently a batch
process in most implementations.
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Assembly language Data directives
1
There are instructions used to define data
elements to hold data and variables. They
define the type of data, the length and the
alignment of data. These instructions can
also define whether the data is available to
outside programs (programs assembled
separately) or only to the program in which
the data section is defined. Some
assemblers classify these as pseudo-ops.
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BlackBerry Ltd Slipstream Data Inc.
Slipstream Data Inc was a network
optimization / data compression /
network acceleration software
company. BlackBerry acquired the
company as a wholly owned subsidiary
on July 11, 2006. The company
continues to operate out of Waterloo.
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BlackBerry Ltd DataViz
On September 8, 2010, DataViz,
Inc.sold their office suite Documents
To Go and other assets to Research In
Motion for $50 million. Subsequently,
the application which allows users to
view and edit Microsoft Word,
Microsoft Excel and Microsoft
PowerPoint was bundled on
BlackBerry Smartphones and tablets.
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Fiber Distributed Data Interface
Fiber Distributed Data Interface
(FDDI) is a standard for data
transmission in a local area network.
It uses optical fiber as its standard
underlying physical medium,
although it was also later specified to
use copper cable, in which case it may
be called CDDI (Copper Distributed
Data Interface), standardized as TPPMD (Twisted-Pair Physical Medium1
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Fiber Distributed Data Interface Description
FDDI provides a 100 Mbit/s optical
standard for data transmission in local
area network that can extend in range up
to 200 kilometers (120 mi)
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Fiber Distributed Data Interface Description
FDDI, as a product of American
National Standards Institute X3T9.5
(now X3T12), conforms to the Open
Systems Interconnection (OSI) model
of functional layering using other
protocols. The standards process
started in the mid 1980s. FDDI-II, a
version of FDDI described in 1989,
added circuit-switched service
capability to the network so that it
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Fiber Distributed Data Interface Description
1
FDDI had a larger maximum-frame size
(4,352 bytes) than the standard
Ethernet family, which only supports a
maximum-frame size of 1,500 bytes,
allowing better effective data rates in
some cases.
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Fiber Distributed Data Interface Topology
1
Designers normally constructed FDDI rings in
a network topology such as a "dual ring of
trees"
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Fiber Distributed Data Interface Topology
FDDI requires this network topology
because the dual ring actually passes
through each connected device and
requires each such device to remain
continuously operational. The standard
actually allows for optical bypasses,
but network engineers consider these
unreliable and error-prone. Devices
such as workstations and
minicomputers that might not come
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Fiber Distributed Data Interface Topology
As an alternative to using a dualattached connection, a workstation can
obtain the same degree of resilience
through a dual-homed connection
made simultaneously to two separate
devices in the same FDDI ring. One of
the connections becomes active while
the other one is automatically blocked.
If the first connection fails, the backup
link takes over with no perceptible
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Fiber Distributed Data Interface Frame format
1
The Internet Engineering Task Force
defined a standard for transmission of
the Internet Protocol (which would be
the protocol data unit in this case)
over FDDI
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Fiber Distributed Data Interface Deployment
FDDI was considered an attractive
campus backbone network technology
in the early to mid 1990s since
existing Ethernet networks only
offered 10 Mbit/s data rates and token
ring networks only offered 4 Mbit/s or
16 Mbit/s rates
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Fiber Distributed Data Interface Standards
ANSI X3.166-1989, Physical
Medium Dependent (PMD) — also
ISO 9314-3
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Fiber Distributed Data Interface Standards
1
ANSI X3.184-1993, Single Mode Fiber Physical
Medium Dependent (SMF-PMD) — also ISO
9314-4
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Data conferencing
Data conferencing refers to a
communication session among two or
more participants sharing computer
data in real time. Interaction and
presentation devices such as a screen,
keyboard, mouse, camera, etc. can be
shared or be able to control each other
computer. It is used to distinguish
from video conferencing and audio
conferencing.
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Data conferencing
1
The data can include screen, documents,
graphics, drawings and applications that
can be seen, annotated or manipulated by
participants.
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Internet Protocol Datagram construction
1
Each datagram has two components: a
header and a payload. The IP header
is tagged with the source IP address,
the destination IP address, and other
meta-data needed to route and deliver
the datagram. The payload is the data
that is transported. This method of
nesting the data payload in a packet
with a header is called encapsulation.
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Debian The dpkg database
1
dpkg is the storage information center
of installed packages and provides no
configuration for accessing online
repositories. The dpkg database is
located at /var/lib/dpkg/available
and contains the list of "installed"
software on the current system.
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comScore Data collection and reporting
1
comScore maintains a group of users who
have monitoring software (with brands
including PermissionResearch,
OpinionSquare and VoiceFive Networks)
installed on their computers. In exchange
for joining the comScore research panels,
users are presented with various benefits,
including computer security software,
Internet data storage, virus scanning and
chances to win cash or prizes.
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comScore Data collection and reporting
1
comScore is up-front about collecting user
data and the software's ability to track all
of a user's internet traffic, including
normally secure (https://) connections
used to communicate banking and other
confidential information.
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comScore Data collection and reporting
1
To ensure the accuracy of the data,
comScore verifies its users'
demographics during the course of
measuring statistical data.
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comScore Data collection and reporting
1
The corrected data is used to generate
reports on topics ranging from web
traffic to video streaming activity and
consumer buying power.
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Automatic Data Processing
1
Automatic Data
Processing
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Automatic Data Processing
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Automatic Data Processing, Inc. (ADP), is
an American provider of business
outsourcing solutions. It is also a provider
of computing services to automobile and
heavy equipment dealers. ADP is one of
four U.S. companies to get a AAA credit
rating from Standard & Poor's (S&P) and
Moody's.
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Automatic Data Processing History
1
In 1957, the company
changed its name to
Automatic Data
Processing, Inc
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Automatic Data Processing History
From 1985 onward, ADP’s annual
revenues exceeded the $1 billion
mark, with paychecks processed for
about 10% of the U.S. workforce. In the
1990s ADP began acting as a
professional employer organization
(PEO). Around this time, the company
acquired Autonom, a German
company, Kerridge Computer Co.
Ltd., a dealer management systems
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Automatic Data Processing History
1
In September 1998, ADP acquired UKbased Chessington Computer Centre
that supplied administration services
to the UK Government.
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Automatic Data Processing History
In 2007, the ADP Brokerage Service
Group was spun off to form Broadridge
Financial Solutions, Inc. (NYSE: BR),
removing about $2 billion from ADP's total
yearly revenue. ADP distributed one share
of Broadridge common stock for every four
shares of ADP common stock held by
shareholders of record as of the close of
business on March 23, 2007.
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Automatic Data Processing History
ADP is sponsor of the ADP National
Employment Report as well as the ADP Small
Business Report.
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Automatic Data Processing Dealer services
Dealer Services provides integrated
technology services and solutions to about
25,000 automotive dealerships
internationally, as well as vehicle
manufacturers. In 2010, ADP acquired the
automotive marketing company Cobalt.
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Automatic Data Processing Dealer services
BZ Results (Automotive Dealer
Services), winner of the 2006
“Innovative Company of the Year”,
was purchased by ADP in 2006. At the
time, BZ Results was valued at $125
million.
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Automatic Data Processing Recognitions
ADP was recently listed as #47 on
Computerworld's 19th annual list of the
100 Best Places to Work in IT 2012.
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Automatic Data Processing Recognitions
1
ADP achieved a 100 percent rating in the 2011
Corporate Equality Index (CEI) report.
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Automatic Data Processing Recognitions
1
In 2009 the Aberdeen Group named ADP
as one of the top 25 Most Influential
Technology Vendors for 2009.
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Automatic Data Processing Recognitions
1
ADP ranked first on Fortune's 2010 list of
America’s Most Admired Companies within
the Financial Data Services category.
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Automatic Data Processing Primary Competitors
1
Ultimate Software
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Bharti Airtel Mobile data service
1
The different services under mobile data
are BlackBerry services,a web-enabled
mobile email solution working on 'Push
Technology', USB modem that helps in
getting instant access to Internet and
corporate applications,Airtel Data Card
that gives the liberty to access the internet
anytime, Easy Mail is a platform that
provides access to personal/corporate emails independent of handset operating
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Datagram Congestion Control Protocol
1
The Datagram Congestion Control Protocol
(DCCP) is a message-oriented transport layer
protocol
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Datagram Congestion Control Protocol
1
DCCP provides a way to gain access to
congestion control mechanisms
without having to implement them at
the application layer. It allows for flowbased semantics like in Transmission
Control Protocol (TCP), but does not
provide reliable in-order delivery.
Sequenced delivery within multiple
streams as in the Stream Control
Transmission Protocol (SCTP) is not
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Datagram Congestion Control Protocol
Currently such applications have often
either settled for TCP or used User
Datagram Protocol (UDP) and
implemented their own congestion control
mechanisms, or have no congestion
control at all.
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Datagram Congestion Control Protocol
1
While being useful for these applications,
DCCP can also be positioned as a general
congestion control mechanism for UDPbased applications, by adding, as needed,
a mechanism for reliable and/or in-order
delivery on the top of UDP/DCCP. In this
context, DCCP allows the use of different,
but generally TCP-friendly congestion
control mechanisms.
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Datagram Congestion Control Protocol
A DCCP connection contains
acknowledgment traffic as well as
data traffic. Acknowledgments inform
a sender whether its packets have
arrived, and whether they were
marked by Explicit Congestion
Notification (ECN).
Acknowledgements are transmitted as
reliably as the congestion control
mechanism in use requires, possibly
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Datagram Congestion Control Protocol
1
DCCP has the option for very long (48-bit)
sequence numbers corresponding to a
packet ID, rather than a byte ID as in TCP.
The long length of the sequence numbers
is intended to guard against "some blind
attacks, such as the injection of DCCPResets into the connection."
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Datagram Congestion Control Protocol DCCP implementations
1
As of June 2008, at least two DCCP
implementations are actively
maintained. The Linux kernel
implementation was first available in
Linux release 2.6.14. The dccp-tp
implementation is optimized for
portability, but has had no changes
since June 2008.
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Datagram Congestion Control Protocol DCCP implementations
Recently, a new user-space
implementation of DCCP has been under
way. The purpose of this implementation is
to provide a standardized, portable NATfriendly framework for peer-to-peer
communications with flexible congestion
control, depending on application.
1
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Google Search Non-indexable data
1
Despite its immense index, there is also a
considerable amount of data available in
online databases which are accessible by
means of queries but not by links. This socalled invisible or deep Web is minimally
covered by Google and other search
engines. The deep Web contains library
catalogs, official legislative documents of
governments, phone books, and other
content which is dynamically prepared to
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Data storage device Terminology
1
Devices that are not used
exclusively for recording
(e.g
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Data storage device Terminology
Many data storage devices are also
media players. Any device that can
store and playback multimedia may
also be considered a media player such
as in the case with the HD media player.
Designated Hard Drives are used to
play saved or streaming media on
home cinemas or home theater PCs.
1
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Data storage device Global capacity, digitization, and trends
1
In a recent study in Science it was
estimated that the world's
technological capacity to store
information in analog and digital
devices grew from less than 3
(optimally compressed) exabytes in
1986, to 295 (optimally compressed)
exabytes in 2007, doubling roughly
every 3 years.
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Data storage device Global capacity, digitization, and trends
In a less comprehensive study, the
International Data Corporation estimated
that the total amount of digital data was
281 exabytes in 2007, and had for the first
time exceeded the amount of storage.
1
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Data storage device Global capacity, digitization, and trends
It is estimated that the year 2002
marked the beginning of the digital
age for information storage, the year
that marked the date when human
kind started to store more information
on digital than on analog storage
devices. In 1986 only 1% of the world's
capacity to store information was in
digital format, which grew to 3% by
1993, 25% in the year 2000, and
1
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Data storage device Data storage equipment
Any input/output equipment may be
considered data storage equipment if it
writes to and reads from a data storage
medium.
1
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Data storage device Data storage methods
semi-, requiring mechanical
disassembly tools and/or opening a
chassis, or
1
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Data storage device Data storage methods
1
'Volatile methods,
meaning loss of
memory if
disconnected from
the unit.
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Data storage device Recording medium
1
A recording medium is a physical material
that holds data expressed in any of the
existing recording formats. With electronic
media, the data and the recording medium
is sometimes referred to as "software"
despite the more common use of the word
to describe computer software. With
(traditional art) static media, art materials
such as crayons may be considered both
equipment and medium as the wax,
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Data storage device Recording medium
Some recording media may be
temporary either by design or by
nature. Volatile organic compounds
may be used to preserve the
environment or to purposely make
data expire over time. Data such as
smoke signals or skywriting are
temporary by nature.
1
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Data storage device Ancient examples
The Gutenberg Bible displayed by the
United States Library of Congress,
demonstrating printed pages as a storage
medium
1
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Data storage device Ancient examples
1
A set of index cards
in a file box are a
nonlinear storage
medium.
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Data storage device Ancient examples
1
Any object visible to the eye, used to mark a
location such as a stone, flag, or skull.
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Data storage device Ancient examples
1
Any crafting material used to form shapes such
as clay, wood, metal, glass, wax, or quipu.
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Data storage device Ancient examples
1
Any hard surface that
could hold carvings.
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Data storage device Ancient examples
1
Any branding surface that would scar under intense
heat (chiefly for livestock or humans).
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Data storage device Ancient examples
Any marking
substance such as
paint, ink, or chalk.
1
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Data storage device Ancient examples
Any surface that would hold a marking
substance such as, papyrus, paper, or skin.
1
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Data storage device Ancient examples
1
DNA – for uses of DNA by people for storing
information, see DNA digital data storage
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Data storage device Modern examples by energy used
1
Graffiti on a public wall. Public surfaces
are being used as unconventional data
storage media, often without
permission.
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Data storage device Modern examples by energy used
1
Photographic film is a
photochemical data
storage medium
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Data storage device Modern examples by energy used
A floppy disk is a
magnetic data storage
medium
1
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Data storage device Modern examples by energy used
A 3.5" PATA Hard Drive is both
storage equipment and a storage
medium.
1
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Data storage device Modern examples by energy used
1
Grooves (See also Audio
Data)
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Data storage device Modern examples by energy used
Drum memory
(magnetic drum)
1
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Data storage device Modern examples by energy used
1
Semiconductor used in volatile
random-access memory
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Data storage device Modern examples by energy used
1
Floating-gate transistor used in
non-volatile memory cards
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Data storage device Modern examples by shape
1
A typical way to classify data storage
media is to consider its shape and type
of movement (or non-movement)
relative to the read/write device(s) of
the storage apparatus as listed:
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Data storage device Modern examples by shape
1
Disk storage (flat,
round, rotating
object)
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Data storage device Modern examples by shape
1
Gramophone record (used for distributing
some 1980s home computer programs)
(mechanical)
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Data storage device Modern examples by shape
1
Magnetic bubble memory
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Data storage device Modern examples by shape
1
Flash memory/memory
card (solid state
semiconductor memory)
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Data storage device Modern examples by shape
1
USB flash drive (also known as a
"thumb drive" or "keydrive")
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Data storage device Modern examples by shape
Sony Memory Stick
(Std/Duo/PRO/MagicG
ate versions)
1
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Data storage device Modern examples by shape
Bekenstein (2003) foresees that
miniaturization might lead to the invention
of devices that store bits on a single atom.
1
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Data storage device Weight and volume
1
Especially for carrying around data,
the weight and volume per MB are
relevant. They are quite large for
written and printed paper compared
with modern electronic media. On the
other hand, written and printer paper
do not require (the weight and volume
of) reading equipment, and
handwritten edits only require simple
writing equipment, such as a pen.
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Data storage device Weight and volume
1
With mobile data connections the data need not
be carried around to be available.
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Data storage device Further reading
Bennett, John C. (1997). 'JISC/NPO
Studies on the Preservation of Electronic
Materials: A Framework of Data Types and
Formats, and Issues Affecting the Long
Term Preservation of Digital Material.
British Library Research and Innovation
Report 50.
1
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Data storage device Further reading
History of Computer
Storage from 1928 to 2013
1
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Chaos theory Distinguishing random from chaotic data
1
It can be difficult to tell from data whether
a physical or other observed process is
random or chaotic, because in practice no
time series consists of pure 'signal.' There
will always be some form of corrupting
noise, even if it is present as round-off or
truncation error. Thus any real time series,
even if mostly deterministic, will contain
some randomness.
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Chaos theory Distinguishing random from chaotic data
All methods for distinguishing
deterministic and stochastic
processes rely on the fact that a
deterministic system always evolves
in the same way from a given starting
point. Thus, given a time series to test
for determinism, one can:
1
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Chaos theory Distinguishing random from chaotic data
1
compare their respective
time evolutions.
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Chaos theory Distinguishing random from chaotic data
1
Define the error as the difference between
the time evolution of the 'test' state and the
time evolution of the nearby state. A
deterministic system will have an error that
either remains small (stable, regular
solution) or increases exponentially with
time (chaos). A stochastic system will have
a randomly distributed error.
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Chaos theory Distinguishing random from chaotic data
1
If the embedding dimension (number
of measures per state) is chosen too
small (less than the 'true' value)
deterministic data can appear to be
random but in theory there is no
problem choosing the dimension too
large – the method will work.
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Chaos theory Distinguishing random from chaotic data
When a non-linear deterministic
system is attended by external
fluctuations, its trajectories present
serious and permanent distortions
1
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Chaos theory Distinguishing random from chaotic data
1
The question of how to distinguish
deterministic chaotic systems from
stochastic systems has also been
discussed in philosophy. It has been
shown that they might be
observationally equivalent.
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AT&T Hemisphere database
1
The company maintains a database of
call detail records of all telephone calls
that have passed through its network
since 1987. AT&T employees work at
High Intensity Drug Trafficking Area
offices (operated by the Office of
National Drug Control Policy) in Los
Angeles, Atlanta, and Houston so data
can be quickly turned over to law
enforcement agencies. Records are
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Data Protection Directive
On 25 January 2012, the European
Commission unveiled a draft European
General Data Protection Regulation that
will supersede the Data Protection
Directive.
1
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Data Protection Directive Context
The right to privacy is a highly
developed area of law in Europe. All
the member states of the European
Union (EU) are also signatories of the
European Convention on Human
Rights (ECHR). Article 8 of the ECHR
provides a right to respect for one's
"private and family life, his home and
his correspondence," subject to
certain restrictions. The European
1
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Data Protection Directive Context
In 1980, in an effort to create a
comprehensive data protection system
throughout Europe, the Organization for
Economic Cooperation and Development
(OECD) issued its “Recommendations of
the Council Concerning Guidelines
Governing the Protection of Privacy and
Trans-Border Flows of Personal Data.”
The seven principles governing the
OECD’s recommendations for protection
1
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Data Protection Directive Context
1
Notice—data subjects should be given notice
when their data is being collected;
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Data Protection Directive Context
1
Consent—data should
not be disclosed
without the data
subject’s consent;
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Data Protection Directive Context
1
Security—collected
data should be kept
secure from any
potential abuses;
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Data Protection Directive Context
1
Disclosure—data subjects should be informed
as to who is collecting their data;
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Data Protection Directive Context
1
Access—data subjects should be allowed
to access their data and make corrections
to any inaccurate data; and
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Data Protection Directive Context
1
Accountability—data subjects should
have a method available to them to
hold data collectors accountable for
following the above principles.
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Data Protection Directive Context
1
The OECD Guidelines, however, were
nonbinding, and data privacy laws still
varied widely across Europe. The US,
meanwhile, while endorsing the OECD’s
recommendations, did nothing to
implement them within the United States.
However, all seven principles were
incorporated into the EU Directive.
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Data Protection Directive Context
1
In 1981 the Convention for the Protection
of Individuals with regard to Automatic
Processing of Personal Data was
negotiated within the Council of Europe.
This convention obliges the signatories to
enact legislation concerning the automatic
processing of personal data, which many
duly did.
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Data Protection Directive Context
1
The European Commission realised that
diverging data protection legislation
amongst EU member states impeded the
free flow of data within the EU and
accordingly proposed the Data Protection
Directive.
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Data Protection Directive Content
1
The directive regulates the processing of
personal data regardless of whether such
processing is automated or not.
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Data Protection Directive Scope
Personal data are defined as "any
information relating to an identified
or identifiable natural person ("data
subject"); an identifiable person is one
who can be identified, directly or
indirectly, in particular by reference
to an identification number or to one
or more factors specific to his
physical, physiological, mental,
economic, cultural or social identity;"
1
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Data Protection Directive Scope
1
This definition is meant to be very broad.
Data are "personal data" when someone is
able to link the information to a person,
even if the person holding the data cannot
make this link. Some examples of
"personal data" are: address, credit card
number, bank statements, criminal record,
etc.
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Data Protection Directive Scope
The notion processing means "any
operation or set of operations which is
performed upon personal data, whether or
not by automatic means, such as
collection, recording, organization,
storage, adaptation or alteration, retrieval,
consultation, use, disclosure by
transmission, dissemination or otherwise
making available, alignment or
combination, blocking, erasure or
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Data Protection Directive Scope
1
The responsibility for compliance rests on
the shoulders of the "controller", meaning
the natural or artificial person, public
authority, agency or any other body which
alone or jointly with others determines the
purposes and means of the processing of
personal data; (art. 2 d)
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Data Protection Directive Scope
1
As a consequence, the website operator would have
to comply with the European data protection rules
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Data Protection Directive Scope
The proposed new European Union
Data Protection Regulation (a draft for
which was unveiled in January 2012)
extends the scope of the EU data
protection law to all foreign
companies processing data of
European Union residents.
1
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Data Protection Directive Principles
1
Personal data should not be processed
at all, except when certain conditions
are met. These conditions fall into
three categories: , legitimate purpose,
and proportionality.
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Data Protection Directive Transparency
The data subject has the right to be
informed when his personal data is being
processed. The controller must provide his
name and address, the purpose of
processing, the recipients of the data and
all other information required to ensure the
processing is fair. (art. 10 and 11)
1
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Data Protection Directive Transparency
Data may be
processed only under
the following
circumstances (art.
7):
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Data Protection Directive Transparency
1
when the data subject
has given his consent
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Data Protection Directive Transparency
1
when processing is necessary in order to
protect the vital interests of the data subject
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Data Protection Directive Transparency
processing is necessary for the
performance of a task carried out in the
public interest or in the exercise of
official authority vested in the
controller or in a third party to whom
the data are disclosed
1
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Data Protection Directive Transparency
The data subject even has the right to
demand the rectification, deletion or
blocking of data that is incomplete,
inaccurate or isn't being processed in
compliance with the data protection rules
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Data Protection Directive Legitimate purpose
1
Personal data can only be processed for
specified explicit and legitimate purposes
and may not be processed further in a way
incompatible with those purposes. (art. 6
b)
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Data Protection Directive Proportionality
Member States shall lay down
appropriate safeguards for personal
data stored for longer periods for
historical, statistical or scientific use
1
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Data Protection Directive Proportionality
1
When sensitive personal data (can be:
religious beliefs, political opinions,
health, sexual orientation, race,
membership of past organisations) are
being processed, extra restrictions
apply. (art. 8)
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Data Protection Directive Proportionality
1
The data subject may object at any time
to the processing of personal data for
the purpose of direct marketing. (art.
14)
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Data Protection Directive Proportionality
1
A decision which produces legal effects or
significantly affects the data subject may
not be based solely on automated
processing of data. (art. 15) A form of
appeal should be provided when
automatic decision making processes are
used.
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Data Protection Directive Supervisory authority and the public register of
processing operations
Each member state must set up a
supervisory authority, an independent
body that will monitor the data
protection level in that member state,
give advice to the government about
administrative measures and
regulations, and start legal proceedings
when data protection regulation has
been violated. (art. 28) Individuals may
lodge complaints about violations to
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Data Protection Directive Supervisory authority and the public register of
processing operations
The controller must notify the
supervisory authority before he starts
to process data. The notification
contains at least the following
information (art. 19):
1
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Data Protection Directive Supervisory authority and the public register of
processing operations
1
a description of the category or categories
of data subject and of the data or
categories of data relating to them;
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Data Protection Directive Supervisory authority and the public register of processing
operations
1
proposed transfers of data to
third countries;
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Data Protection Directive Supervisory authority and the public register of
processing operations
1
a general description of the measures taken to
ensure security of processing.
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Data Protection Directive Supervisory authority and the public register of
processing operations
1
This information is kept
in a public register.
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Data Protection Directive Transfer of personal data to third countries
1
Third countries is the term used in
legislation to designate countries
outside the European Union. Personal
data may only be transferred to third
countries if that country provides an
adequate level of protection. Some
exceptions to this rule are provided,
for instance when the controller
himself can guarantee that the
recipient will comply with the data
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Data Protection Directive Transfer of personal data to third countries
1
The Directive's Article 29 created the
"Working party on the Protection of
Individuals with regard to the
Processing of Personal Data,"
commonly known as the "Article 29
Working Party". The Working Party
gives advice about the level of
protection in the European Union and
third countries.
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Data Protection Directive Transfer of personal data to third countries
1
The Working Party negotiated with U.S.
representatives about the protection of
personal data, the Safe Harbor
Principles were the result. According
to critics the Safe Harbor Principles do
not provide for an adequate level of
protection, because they contain fewer
obligations for the controller and
allow the contractual waiver of certain
rights.
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Data Protection Directive Transfer of personal data to third countries
1
In July 2007, a new, controversial,
Passenger Name Record agreement
between the US and the EU was
undersigned.
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Data Protection Directive Transfer of personal data to third countries
1
The tensions between Washington and
Brussels are mainly caused by the lower
level of data protection in the US,
especially since foreigners do not benefit
from the US Privacy Act of 1974
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Data Protection Directive Implementation by the member states
EU directives are addressed to the
member states, and aren't legally
binding for citizens in principle. The
member states must transpose the
directive into internal law. Directive
95/46/EC on the protection of personal
data had to be transposed by the end
of 1998. All member states have
enacted their own data protection
legislation.
1
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Data Protection Directive Comparison with US data protection law
1
Privacy legislation in the United States
tends to be adopted on an ad hoc
basis, with legislation arising when
certain sectors and circumstances
require (e.g., the Video Privacy
Protection Act of 1988, the Cable
Television Protection and Competition
Act of 1992, the Fair Credit Reporting
Act, and the 2010 Massachusetts Data
Privacy Regulations)
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Data Protection Directive Comparison with US data protection law
1
The reasoning behind this approach
probably has as much to do with
American laissez-faire economics as
with different social perspectives. The
First Amendment of the United States
Constitution guarantees the right to
free speech. While free speech is an
explicit right guaranteed by the
United States Constitution, privacy is
an implicit right guaranteed by the
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Data Protection Directive Comparison with US data protection law
1
Germany and France, in particular, set forth
comprehensive data protection laws
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Data Protection Directive Comparison with US data protection law
1
Phil Zimmermann has called the EU's
requirement of data retention worse
for the individual than the ad-hoc
policies of the USA.
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Datagram
A datagram is a basic transfer unit
associated with a packet-switched network
in which the delivery, arrival time, and
order of arrival are not guaranteed by the
network service.
1
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Datagram Origin of the term
1
The term datagram appeared first within
the project CYCLADES, a packet
switching network created in the early
1970s, and was coined by Louis Pouzin by
combining the words data and telegram.
CYCLADES was the first network to make
the hosts responsible for the reliable
delivery of data, rather than the network
itself, using unreliable datagrams and
associated end-to-end protocol
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Datagram Origin of the term
1
“The inspiration for datagrams had
two sources. One was Donald Davies'
studies. He had done some simulation
of datagram networks, although he
had not built any, and it looked
technically viable. The second
inspiration was I like things simple. I
didn't see any real technical
motivation to overlay two levels of
end-to-end protocols. I thought one
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Datagram Definition
1
A datagram or packet needs to be selfcontained without reliance on earlier
exchanges because there is no
connection of fixed duration between
the two communicating points as there
is, for example, in most voice telephone
conversations.
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Datagram Definition
Datagram is
opposite of Virtual
circuit.[page
needed]
1
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Datagram Definition
1
“A self-contained, independent entity
of data carrying sufficient information
to be routed from the source to the
destination computer without reliance
on earlier exchanges between this
source and destination computer and
the transporting network.”
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Datagram Definition
1
Datagram service is often compared
to a mail delivery service, the user
only provides the destination address,
but receives no guarantee of delivery,
and no confirmation upon successful
delivery.
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Datagram Definition
1
Datagram service routes datagrams
without first creating a predetermined
path. There is also no consideration
given to the order in which it and other
datagrams are sent or received. In
fact, many datagrams in the same
group can travel along different paths
before reaching the same destination.
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Datagram Structure of a datagram
1
Each datagram has two components, a
header and a data payload. The header
contains all the information sufficient for
routing from the originating equipment to
the destination without relying on prior
exchanges between the equipment and
the network. Headers may include source
and destination addresses as well as a
type field. The payload is the data to be
transported. This process of nesting data
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Datagram Use in the Internet Protocol
Datagram service is a service
provided by IP at the Internet layer. It
is a connectionless, best effort,
unreliable, message delivery service.
Many higher level protocols including
TCP (a connection-oriented service)
depend on IP's Datagram service,
laying additional functionality on top.
UDP uses IP's Datagram service as
well.
1
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Datagram Use in the Internet Protocol
The term datagram is often
considered synonymous to packet but
there are some nuances. The term
datagram is generally reserved for
packets of an unreliable service that
does not notify the user if delivery
fails, while the term packet applies to
any message formatted as a packet.
For example, Internet Protocol (IP)
provides an unreliable service and
1
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Datagram Use in the Internet Protocol
If a datagram fragments, then its
fragments may be referred to as
packets, but not as datagrams. TCP
refers to its fragments as TCP
segments, not packets, presumably to
distinguish them from unreliable
fragments.
1
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Apache Cassandra Data model
1
This section requires expansion with: informational
details and clarification.
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Apache Cassandra Data model
1
Cassandra is essentially a hybrid between a keyvalue and a column-oriented (or tabular) database.
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Apache Cassandra Data model
1
A column family resembles a table in an
RDBMS. Column families contain rows
and columns. Each row is uniquely
identified by a row key. Each row has
multiple columns, each of which has a
name, value, and a timestamp. Unlike a
table in an RDBMS, different rows in the
same column family do not have to share
the same set of columns, and a column
may be added to one or multiple rows at
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Apache Cassandra Data model
1
Each key in Cassandra corresponds to a
value which is an object. Each key has
values as columns, and columns are
grouped together into sets called column
families.
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Apache Cassandra Data model
Thus, each key identifies a row of a
variable number of elements. These
column families could be considered then
as tables. A table in Cassandra is a
distributed multi dimensional map indexed
by a key.
1
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Apache Cassandra Data model
1
Furthermore, applications can specify the
sort order of columns within a Super
Column or Simple Column family.
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Control Data Corporation
1
After several years of losses in the early
1980s, CDC made the decision to leave
the computer manufacturing business and
sell those parts of the company in 1988, a
process that was completed in 1992 with
the creation of Control Data Systems, Inc.
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Control Data Corporation Background and origins: World War II–1957
During World War II the U.S. Navy had
built up a team of engineers to build
codebreaking machinery for both
Japanese and German electro-mechanical
ciphers. A number of these were produced
by a team dedicated to the task working in
the Washington, D.C., area. With the postwar wind-down of military spending, the
Navy grew increasingly worried that this
team would break up and scatter into
1
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Control Data Corporation Background and origins: World War II–1957
1
Eventually they found their solution; the owner
of a Chase Aircraft affiliate in St
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Control Data Corporation Background and origins: World War II–1957
The result was Engineering Research
Associates (ERA), a contract engineering
company that worked on a number of
seemingly unrelated projects in the early
1950s
1
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Control Data Corporation Background and origins: World War II–1957
1
Although Rand kept the ERA team
together and developing new
products, it was most interested in
ERA's magnetic drum memory
systems
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Control Data Corporation Background and origins: World War II–1957
1
Seymour Cray soon became the chief
designer, though at the time of CDC's
formation he was still in the process of
completing a prototype for the Naval
Tactical Data System (NTDS), and he
did not leave Sperry to join CDC until
it was complete.
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Control Data Corporation Early designs and Cray's big plan
CDC started business by selling
subsystems, mostly drum memory
systems, to other companies. Cray
joined the next year, and he
immediately built a small transistorbased 6-bit machine known as the
"CDC Little Character" to test his ideas
on large-system design and transistorbased machines. "Little Character" was
a great success.
1
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Control Data Corporation Early designs and Cray's big plan
In 1959 CDC released a 48-bit
transistorized version of their 1103 redesign as the CDC 1604, with the first
machine delivered to the U.S. Navy in
1960. Legend has it that the 1604
designation was chosen by adding
CDC's first street address (501 Park
Avenue) to Cray's former project, the
ERA-Univac 1103.
1
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Control Data Corporation Early designs and Cray's big plan
A 12-bit cut-down version was also
released as the CDC 160A in 1960, often
considered among the first
minicomputers. The 160A was
particularly notable as it was built as a
standard office desk, which was
unusual packaging for that era. New
versions of the basic 1604 architecture
were rebuilt into the CDC 3000 series,
which sold through the early and mid1
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Control Data Corporation Early designs and Cray's big plan
Cray immediately turned to the
design of a machine that would be the
fastest (or in the terminology of the
day, largest) machine in the world,
setting the goal at 50 times the speed
of the 1604
1
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Control Data Corporation Peripherals business
1
In the early 1960s, the corporation moved to Ford
Parkway in the Highland Park neighborhood of St
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Control Data Corporation Peripherals business
1
One of its first peripherals was a tape
transport, which led to some internal
wrangling as the Peripherals
Equipment Division attempted to find a
reasonable way to charge other
divisions of the company for supplying
the devices
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Control Data Corporation Peripherals business
1
The tape transport was followed by the
405 Card Reader and the 415 Card
Punch, followed by a series of tape
drives and drum printers, all of which
were designed in-house. The printer
business was initially supported by
Holley Carburetor in the Rochester,
Michigan suburb outside of Detroit.
They later formalized this by creating
a jointly held company, Holley
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Control Data Corporation Peripherals business
1
Norris was particularly interested in
breaking out of the punched card–
based workflow, where IBM held a
stranglehold
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Control Data Corporation Peripherals business
1
With the continued delays on the OCR
project, it became clear that punched
cards were not going to go away any
time soon, and CDC had to address
this as quickly as possible
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Control Data Corporation Peripherals business
Another side-effect of Norris's attempts to
diversify was the creation of a number of
service bureaus that ran jobs on behalf of
smaller companies that could not afford to
buy computers. This was never very
profitable, and in 1965, several managers
suggested that the unprofitable centers be
closed in a cost-cutting measure.
Nevertheless, Norris was so convinced of the
idea that he refused to accept this, and
ordered an across-the-board "belt tightening"
instead.
1
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Control Data Corporation CDC 6600: defining supercomputing
1
Meanwhile at the new Chippewa Falls
lab, Seymour Cray, Jim Thornton, and
Dean Roush put together a team of 34
engineers, which continued work on
the new computer design
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Control Data Corporation CDC 6600: defining supercomputing
1
That way the CPU could devote all of
its time and circuitry to processing
actual data, while the other
controllers dealt with the mundane
tasks like punching cards and
running disk drives
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Control Data Corporation CDC 6600: defining supercomputing
A Fortran compiler known as MNF
(Minnesota Fortran) was developed by
Lawrence A. Liddiard and E. James
Mundstock at the University of
Minnesota for the 6600.
1
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Control Data Corporation CDC 6600: defining supercomputing
1
It was after the delivery of the 6600 that IBM took
notice of this new company
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Control Data Corporation CDC 6600: defining supercomputing
1
In the meantime, IBM announced a new
version of the famed System/360, the
Model 92, which would be just as fast
as CDC's 6600
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Control Data Corporation CDC 6600: defining supercomputing
1
During the designing of the 6600, CDC
had set up Project SPIN to supply the
system with a high speed hard disk
memory system
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Control Data Corporation CDC 7600 and 8600
In the same month it won its lawsuit
against IBM, CDC also announced its new
computer, the CDC 7600 (previously
referred to as the 6800 within CDC). This
machine's hardware clock speed was
almost four times that of the 6600 (36 MHz
vs. 10 MHz) a 27.5 ns clock cycle, and it
offered considerably more than four times
the total throughput.
1
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Control Data Corporation CDC 7600 and 8600
1
Much of this speed increase was due
to extensive use of pipelining, a
technique that allows different parts
of the CPU to work simultaneously on
different parts of successive
instructions of the process at the same
time
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Control Data Corporation CDC 7600 and 8600
The 7600 did not do well in the
marketplace because it was introduced
in the 1969 downturn in the U.S
1
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Control Data Corporation CDC 7600 and 8600
1
Cray then turned to the
design of the CDC 8600
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Control Data Corporation The STAR and the Cyber
In addition to the redesign of the 8600,
CDC had another project called the CDC
STAR-100 underway, led by Cray's former
collaborator on the 6600/7600, Jim
Thornton
1
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Control Data Corporation The STAR and the Cyber
Since these two projects competed for
limited funds during the late 1960s, Norris
felt that the company could not support
simultaneous development of the STAR
and a complete redesign of the 8600
1
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Control Data Corporation The STAR and the Cyber
A variety of systems based on the basic
6600/7600 architecture were repackaged
in different price/performance categories
of the CDC Cyber, which became CDC's
main product line in the 1970s. An updated
version of the STAR architecture, the
Cyber 205, had considerably better
performance than the original. By this
time, however, Cray's own designs, like
the Cray-1, were using the same basic
1
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Control Data Corporation The STAR and the Cyber
Sales of the STAR were weak, but
Control Data Corp. produced a
successor system, the Cyber 200/205,
that gave Cray Research some
competition. CDC also embarked on a
number of special projects for its
clients, who produced an even smaller
number of black project computers.
The CDC Advanced Flexible
Processor (AFP), also known as
1
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Control Data Corporation The STAR and the Cyber
Another design direction was the
"Cyber 80" project, which was aimed
at release in 1980. This machine could
run old 6600-style programs, and also
had a completely new 64-bit
architecture. The concept behind
Cyber 80 was that current 6000-series
users would migrate to these
machines with relative ease. The
design and debugging of these
1
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Control Data Corporation The STAR and the Cyber
1
CDC was also attempting to diversify its
revenue from hardware into services and
this included its promotion of the PLATO
(computer system) computer-aided
learning system, which ran on Cyber
hardware and incorporated many early
computer interface innovations including
bit-mapped touchscreen terminals.
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Control Data Corporation Magnetic Peripherals Inc.
1
Magnetic Peripherals Inc., originally
a joint venture with Honeywell and
Honeywell Bull, became a major
player in the hard disk drive market
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Control Data Corporation Magnetic Peripherals Inc.
1
CDC founded a separate division called
Rigidyne in Simi Valley, California, to
develop 3.5-inch drives using technology
from the Wren series. These were
marketed by CDC as the "Swift" series,
and were some of the first highperformance 3.5-inch drives on the market
at their introduction in 1987.
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Control Data Corporation Magnetic Peripherals Inc.
in September 1988, CDC merged
Rigidyn and MPI into the umbrella
subsidiary of Imprimis Technology.
The next year, Seagate Technology
purchased Imprimis.
1
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Control Data Corporation ETA Systems, wind-down and sale of assets
1
So in 1983, he set up a spinoff company,
ETA Systems, whose design goal was a
machine processing data at 10 GFLOPs,
about 40 times the speed of the Cray-1
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Control Data Corporation ETA Systems, wind-down and sale of assets
1
Finally, in 1992, the computer hardware
and service businesses were spun out
as Control Data Systems, Inc
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Control Data Corporation ETA Systems, wind-down and sale of assets
1
CDC's Energy Management Division
was one of the most successful CDC
business units, providing control
systems solutions that managed as
much as 25% of all electricity on the
planet. In 1988 or 1989 this division
was renamed Empros and was later
sold to Siemens as CDC broke apart.
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Control Data Corporation ETA Systems, wind-down and sale of assets
1
Finally, after the CDS spinout, all that was
left of CDC was its services business, and
it became known as the Ceridian
Corporation. Ceridian continues as a
successful outsourced IT company
focusing on human resources. In 1997
General Dynamics acquired the
Computing Devices International Division
of Ceridian. Computing Devices,
headquartered in Bloomington, Minnesota,
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Control Data Corporation Commercial Credit Corporation
In 1986, Sandy Weill convinced the
Control Data management to spin off their
Commercial Credit subsidiary to prevent
the company's potential liquidation
1
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Control Data Corporation Notes
1
Jump up ^ "Ceridian, Form PRE 14A,
Filing Date Mar 4, 1994".
secdatabase.com. Retrieved Mar 28,
2013.
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Control Data Corporation Notes
Jump up ^ "Ceridian, Form 10-K,
Annual Report, Filing Date Mar 10,
1994". secdatabase.com. Retrieved Mar
28, 2013.
1
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Control Data Corporation Notes
1
Jump up ^ Curiously, a very detailed
1975 oral history with CDC's
computer engineers does not confirm
this legend: when the "1604" question
was asked, the insiders laughed and
responded: "It was quite popular at
the time that this was the origin."
Page 21 of the oral history provides
the official CDC explanation for 1604.
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Control Data Corporation Notes
1
Jump up ^ See story of a
salesman's uninvited
visit to Chippewa Falls
here.
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Control Data Corporation Notes
Jump up ^ The Supermen,
Charles Murray, John Wiley and
Sons, 1997.
1
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Control Data Corporation Notes
1
Jump up ^ Frisch, Michael (Dec 1972).
"Remarks on Algorithms".
Communications of the ACM 15 (12):
1074.
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Control Data Corporation Notes
1
Jump up ^ Oral history interview with
Richard G. Lareau, Charles Babbage
Institute, University of Minnesota.
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Control Data Corporation Notes
1
Jump up ^ "COMPUTERS: A Settlement for IBM",
Time magazine, Monday, Jan. 29, 1973.
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Control Data Corporation Notes
Norris, Control Data's one-man-gang
chairman, said that the daring suit had
turned out to be 'one of the best
management decisions in our history.' ..."
1
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Control Data Corporation Notes
Jump up ^
http://www.3m.com/us/healthcare/p
rofessionals/wound_care/downloads/
3m_healthcare_history.pdf
1
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Control Data Corporation Notes
1
Jump up ^ Price, Robert (2005-11-11). The
Eye for Innovation: Recognizing
Possibilities and Managing the Creative
Enterprise 11 (1 ed.). New Haven, Ct: Yale
University Press. p. 168. ISBN 978-0-30010877-4.
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Control Data Corporation Notes
Jump up ^ "Citicorp, Form 10-Q,
Quarterly Report, Filing Date Nov 12,
1999". secdatabase.com. Retrieved Mar
28, 2013.
1
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Computer security Hardware mechanisms that protect computers and data
While hardware may be a source of
insecurity, hardware based or assisted
computer security also offers an
alternative to software-only computer
security. Devices such as dongles,
trusted platform modules, case
intrusion detection, drive locks,
disabling USB ports, and other
hardware may be considered more
secure due to the physical access (or
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Data center History
1
Data centers have their roots in the
huge computer rooms of the early
ages of the computing industry
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Data center History
1
The use of the term "data center," as
applied to specially designed computer
rooms, started to gain popular
recognition about this time.
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Data center History
1
But nowadays, the division of these terms
has almost disappeared and they are
being integrated into a term "data center."
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Data center History
Data centers are
typically very
expensive to build and
maintain.
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Data center Power and cooling analysis
Among other things, a power and
cooling analysis can help to identify
hot spots, over-cooled areas that can
handle greater power use density, the
breakpoint of equipment loading, the
effectiveness of a raised-floor
strategy, and optimal equipment
positioning (such as AC units) to
balance temperatures across the data
center
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Asus Pirated software and dissemination of confidential data
In September 2008, PC Pro discovered
through a reader that Asus had
accidentally shipped laptops that
contained cracked and pirated software.
Both physical machines and recovery CDs
contained confidential documents from
Microsoft and other organizations, internal
Asus documents, and sensitive personal
information including CVs.
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Asus Pirated software and dissemination of confidential data
At the time, an Asus spokesperson
promised an investigation at "quite a
high level", but declined to comment
on how the files got on the machines
and recovery media. It was
demonstrated that an unattended
installation of Windows Vista could
accidentally copy material from a
flash drive with a parameter in the
"unattend.xml" file on the personal
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Data center
1
Large data centers are industrial scale
operations using as much electricity
as a small town and sometimes are a
significant source of air pollution in
the form of diesel exhaust.
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Data center Requirements for modern data centers
1
A data center must therefore keep high
standards for assuring the integrity and
functionality of its hosted computer
environment
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Data center Requirements for modern data centers
The Telecommunications Industry
Association's TIA-942 Telecommunications
Infrastructure Standard for Data Centers,
specifies the minimum requirements for
telecommunications infrastructure of data
centers and computer rooms including single
tenant enterprise data centers and multitenant Internet hosting data centers. The
topology proposed in this document is
intended to be applicable to any size data
center.
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Data center Requirements for modern data centers
Telcordia GR-3160, NEBS
Requirements for
Telecommunications Data Center
Equipment and Spaces, provides
guidelines for data center spaces
within telecommunications networks,
and environmental requirements for
the equipment intended for
installation in those spaces. These
criteria were developed jointly by
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Data center Requirements for modern data centers
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Operate and manage a carrier’s
telecommunication network
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Data center Requirements for modern data centers
Provide data center
based applications
directly to the carrier’s
customers
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Data center Requirements for modern data centers
1
Provide hosted applications for a third party to
provide services to their customers
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Data center Requirements for modern data centers
1
Effective data center operation requires a
balanced investment in both the facility
and the housed equipment. The first step
is to establish a baseline facility
environment suitable for equipment
installation. Standardization and
modularity can yield savings and
efficiencies in the design and construction
of telecommunications data centers.
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Data center Requirements for modern data centers
For these reasons,
telecommunications data centers
should be planned in repetitive
building blocks of equipment, and
associated power and support
(conditioning) equipment when
practical
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Data center Requirements for modern data centers
In addition to the energy savings,
reduction in staffing costs and the
ability to locate the site further from
population centers, implementing a
lights-out data center reduces the
threat of malicious attacks upon the
infrastructure.
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Data center Requirements for modern data centers
1
There is a trend to modernize data centers
in order to take advantage of the
performance and energy efficiency
increases of newer IT equipment and
capabilities, such as cloud computing. This
process is also known as data center
transformation.
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Data center Requirements for modern data centers
1
Organizations are experiencing rapid IT
growth but their data centers are aging.
Industry research company International
Data Corporation (IDC) puts the average
age of a data center at nine years old.
Gartner, another research company says
data centers older than seven years are
obsolete.
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Data center Requirements for modern data centers
In May 2011, data center research
organization Uptime Institute, reported that
36 percent of the large companies it
surveyed expect to exhaust IT capacity
within the next 18 months.
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Data center Requirements for modern data centers
1
Data center transformation takes a stepby-step approach through integrated
projects carried out over time. This differs
from a traditional method of data center
upgrades that takes a serial and siloed
approach. The typical projects within a
data center transformation initiative
include standardization/consolidation,
virtualization, automation and security.
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Data center Requirements for modern data centers
Standardization/consolidation: The
purpose of this project is to reduce the
number of data centers a large
organization may have. This project also
helps to reduce the number of hardware,
software platforms, tools and processes
within a data center. Organizations replace
aging data center equipment with newer
ones that provide increased capacity and
performance. Computing, networking and
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Data center Requirements for modern data centers
Data released by investment bank
Lazard Capital Markets reports that 48
percent of enterprise operations will
be virtualized by 2012
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Data center Requirements for modern data centers
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Automating: Data center automation
involves automating tasks such as
provisioning, configuration, patching,
release management and compliance.
As enterprises suffer from few skilled
IT workers, automating tasks make
data centers run more efficiently.
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Data center Requirements for modern data centers
1
Securing: In modern data centers, the
security of data on virtual systems is
integrated with existing security of
physical infrastructures. The security
of a modern data center must take into
account physical security, network
security, and data and user security.
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Data center Carrier neutrality
1
Today many data centers are run by
Internet service providers solely for the
purpose of hosting their own and third
party servers.
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Data center Carrier neutrality
1
However traditionally data centers
were either built for the sole use of
one large company (i.e. Google,
Amazon etc.) or as carrier hotels or
Network-neutral data centers.
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Data center Carrier neutrality
1
These facilities enable interconnection
of carriers and act as regional fiber
hubs serving local business in addition
to hosting content servers.
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Data center Data center tiers
1
Another consideration is the placement of
the data center in a subterranean context,
for data security as well as environmental
considerations such as cooling
requirements.
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Data center Data center tiers
1
The German Datacenter star audit
program uses an auditing process to
certify 5 levels of "gratification" that
affect Data Center criticality.
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Data center Data center tiers
Independent from the ANSI/TIA-942
standard, the Uptime Institute, a think tank
and professional-services organization
based in Santa Fe, New Mexico, has
defined its own four levels. The levels
describe the availability of data from the
hardware at a location. The higher the tier,
the greater the availability. The levels are:
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Data center Data center tiers
The difference between 99.671%,
99.741%, 99.982%, and 99.995%, while
seemingly nominal, could be significant
depending on the application.
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Data center Data center tiers
Whilst no down-time is ideal, the tier
system allows the below durations for
services to be unavailable within one year
(525,600 minutes):
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Data center Data center tiers
1
Tier 1 (99.671%) status would allow
1729.224 minutes
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Data center Data center tiers
Tier 2 (99.741%)
status would allow
1361.304 minutes
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Data center Data center tiers
Tier 3 (99.982%) status
would allow 94.608 minutes
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Data center Data center tiers
1
Tier 4 (99.995%) status
would allow 26.28
minutes
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Data center Design considerations
Very large data centers may use
shipping containers packed with 1,000
or more servers each; when repairs or
upgrades are needed, whole
containers are replaced (rather than
repairing individual servers).
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Data center Design considerations
1
Local building codes
may govern the
minimum ceiling
heights.
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Data center Design programming
Other than the architecture of the
building itself there are three
elements to design programming for
data centers: facility topology design
(space planning), engineering
infrastructure design (mechanical
systems such as cooling and electrical
systems including power) and
technology infrastructure design
(cable plant)
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Data center Design programming
1
Various vendors who provide data center
design services define the steps of data
center design slightly differently, but all
address the same basic aspects as given
below.
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Data center Modeling criteria
Modeling criteria are used to develop
future-state scenarios for space, power,
cooling, and costs. The aim is to create a
master plan with parameters such as
number, size, location, topology, IT floor
system layouts, and power and cooling
technology and configurations.
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Data center Design recommendations
1
Design recommendations/plans generally
follow the modelling criteria phase. The
optimal technology infrastructure is
identified and planning criteria is
developed, such as critical power
capacities, overall data center power
requirements using an agreed upon PUE
(power utilization efficiency), mechanical
cooling capacities, kilowatts per cabinet,
raised floor space, and the resiliency level
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Data center Conceptual design
Conceptual designs embody the design
recommendations or plans and should
take into account “what-if” scenarios to
ensure all operational outcomes are met in
order to future-proof the facility.
Conceptual floor layouts should be driven
by IT performance requirements as well as
lifecycle costs associated with IT demand,
energy efficiency, cost efficiency and
availability. Future-proofing will also
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Data center Detail design
Detail design is undertaken once the
appropriate conceptual design is
determined, typically including a proof of
concept. The detail design phase should
include the development of facility
schematics and construction documents
as well as schematic of technology
infrastructure, detailed IT infrastructure
design and IT infrastructure
documentation.
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Data center Mechanical engineering infrastructure design
1
Mechanical engineering infrastructure
design addresses mechanical systems
involved in maintaining the interior
environment of a data center, such as
heating, ventilation and air conditioning
(HVAC); humidification and
dehumidification equipment;
pressurization; and so on
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Data center Electrical engineering infrastructure design
1
Electrical Engineering infrastructure
design is focused on designing
electrical configurations that
accommodate various reliability
requirements and data center sizes.
Aspects may include utility service
planning; distribution, switching and
bypass from power sources;
uninterruptable power source (UPS)
systems; and more.
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Data center Electrical engineering infrastructure design
1
These designs should dovetail to energy
standards and best practices while also
meeting business objectives. Electrical
configurations should be optimized and
operationally compatible with the data
center user’s capabilities. Modern
electrical design is modular and scalable,
and is available for low and medium
voltage requirements as well as DC (direct
current).
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Data center Technology infrastructure design
1
There are cabling systems for all data
center environments, including
horizontal cabling, voice, modem, and
facsimile telecommunications
services, premises switching
equipment, computer and
telecommunications management
connections, keyboard/video/mouse
connections and data communications
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Data center Availability expectations
1
In other words, how can an appropriate
level of availability best be met by design
criteria to avoid financial and operational
risks as a result of downtime? If the
estimated cost of downtime within a
specified time unit exceeds the amortized
capital costs and operational expenses, a
higher level of availability should be
factored into the data center design
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Data center Site selection
1
For example, the topology and the cost of
managing a data center in a warm, humid
climate will vary greatly from managing
one in a cool, dry climate.
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Data center Modularity and flexibility
1
Modularity and flexibility are key elements
in allowing for a data center to grow and
change over time. Data center modules
are pre-engineered, standardized building
blocks that can be easily configured and
moved as needed.
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Data center Modularity and flexibility
1
A modular data center may consist of data
center equipment contained within
shipping containers or similar portable
containers. But it can also be described as
a design style in which components of the
data center are prefabricated and
standardized so that they can be
constructed, moved or added to quickly as
needs change.
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Data center Environmental control
Subterranean data centers may keep
computer equipment cool while expending
less energy than conventional designs.
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Data center Environmental control
Modern data centers try to use
economizer cooling, where they use
outside air to keep the data center cool. At
least one data center (located in Upstate
New York) will cool servers using outside
air during the winter. They do not use
chillers/air conditioners, which creates
potential energy savings in the millions.
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Data center Environmental control
Telcordia GR-2930, NEBS: Raised
Floor Generic Requirements for
Network and Data Centers, presents
generic engineering requirements for
raised floors that fall within the strict
NEBS guidelines.
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Data center Environmental control
There are many types of
commercially available floors that
offer a wide range of structural
strength and loading capabilities,
depending on component construction
and the materials used. The general
types of raised floors include
stringerless, stringered, and
structural platforms, all of which are
discussed in detail in GR-2930 and
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Data center Environmental control
1
Stringerless raised floors - One nonearthquake type of raised floor
generally consists of an array of
pedestals that provide the necessary
height for routing cables and also
serve to support each corner of the
floor panels
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Data center Environmental control
Stringered raised floors - This type of
raised floor generally consists of a vertical
array of steel pedestal assemblies (each
assembly is made up of a steel base plate,
tubular upright, and a head) uniformly
spaced on two-foot centers and
mechanically fastened to the concrete
floor. The steel pedestal head has a stud
that is inserted into the pedestal upright
and the overall height is adjustable with a
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Data center Environmental control
Structural platforms - One type of
structural platform consists of
members constructed of steel angles or
channels that are welded or bolted
together to form an integrated platform
for supporting equipment. This design
permits equipment to be fastened
directly to the platform without the
need for toggle bars or supplemental
bracing. Structural platforms may or
may not contain panels or stringers.
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Data center Metal whiskers
1
This phenomenon is not unique to data
centers, and has also caused
catastrophic failures of satellites and
military hardware.
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Data center Electrical power
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Backup power consists of one or more
uninterruptible power supplies,
battery banks, and/or diesel / gas
turbine generators.
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Data center Electrical power
To prevent single points of failure, all
elements of the electrical systems,
including backup systems, are typically
fully duplicated, and critical servers are
connected to both the "A-side" and "Bside" power feeds. This arrangement is
often made to achieve N+1 redundancy in
the systems. Static switches are
sometimes used to ensure instantaneous
switchover from one supply to the other in
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Data center Electrical power
1
Data centers typically have raised flooring
made up of 60 cm (2 ft) removable square
tiles. The trend is towards 80–100 cm (31–
39 in) void to cater for better and uniform
air distribution. These provide a plenum for
air to circulate below the floor, as part of
the air conditioning system, as well as
providing space for power cabling.
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Data center Low-voltage cable routing
1
Data cabling is typically routed through
overhead cable trays in modern data
centers. But some are still recommending
under raised floor cabling for security
reasons and to consider the addition of
cooling systems above the racks in case
this enhancement is necessary.
Smaller/less expensive data centers
without raised flooring may use anti-static
tiles for a flooring surface. Computer
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Data center Fire protection
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Passive fire protection elements include
the installation of fire walls around the data
center, so a fire can be restricted to a
portion of the facility for a limited time in
the event of the failure of the active fire
protection systems
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Data center Security
Physical security also plays a large
role with data centers. Physical access
to the site is usually restricted to
selected personnel, with controls
including bollards and mantraps.
Video camera surveillance and
permanent security guards are almost
always present if the data center is
large or contains sensitive
information on any of the systems
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Data center Energy use
1
By 2012 the cost of power for the data
center is expected to exceed the cost
of the original capital investment.
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Data center Greenhouse gas emissions
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Given a business as usual scenario
greenhouse gas emissions from data
centers is projected to more than
double from 2007 levels by 2020.
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Data center Greenhouse gas emissions
1
Siting is one of the factors that affect the
energy consumption and environmental
effects of a datacenter. In areas where
climate favors cooling and lots of
renewable electricity is available the
environmental effects will be more
moderate. Thus countries with favorable
conditions, such as: Canada, Finland,
Sweden and Switzerland, are trying to
attract cloud computing data centers.
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Data center Greenhouse gas emissions
1
In an 18-month investigation by scholars
at Rice University’s Baker Institute for
Public Policy in Houston and the Institute
for Sustainable and Applied Infodynamics
in Singapore, data center-related
emissions will more than triple by 2020.
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Data center Energy efficiency
The most commonly used metric to
determine the energy efficiency of a
data center is power usage
effectiveness, or PUE. This simple ratio
is the total power entering the data
center divided by the power used by
the IT equipment.
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Data center Energy efficiency
Some large data center operators like
Microsoft and Yahoo! have published
projections of PUE for facilities in
development; Google publishes quarterly
actual efficiency performance from data
centers in operation.
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Data center Energy efficiency
1
The U.S. Environmental Protection Agency
has an Energy Star rating for standalone
or large data centers. To qualify for the
ecolabel, a data center must be within the
top quartile of energy efficiency of all
reported facilities.
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Data center Energy efficiency
1
European Union also has a similar initiative: EU
Code of Conduct for Data Centres
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Data center Energy use analysis
1
Often, the first step toward curbing energy
use in a data center is to understand how
energy is being used in the data center.
Multiple types of analysis exist to measure
data center energy use. Aspects
measured include not just energy used by
IT equipment itself, but also by the data
center facility equipment, such as chillers
and fans.
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Data center Energy efficiency analysis
An energy efficiency analysis
measures the energy use of data
center IT and facilities equipment. A
typical energy efficiency analysis
measures factors such as a data
center’s power use effectiveness (PUE)
against industry standards, identifies
mechanical and electrical sources of
inefficiency, and identifies airmanagement metrics.
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Data center Computational fluid dynamics (CFD) analysis
By predicting the effects of these
environmental conditions, CFD
analysis in the data center can be used
to predict the impact of high-density
racks mixed with low-density racks
and the onward impact on cooling
resources, poor infrastructure
management practices and AC failure
of AC shutdown for scheduled
maintenance.
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Data center Thermal zone mapping
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Thermal zone mapping uses sensors and
computer modeling to create a threedimensional image of the hot and cool
zones in a data center.
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Data center Thermal zone mapping
This information can help to identify
optimal positioning of data center
equipment. For example, critical servers
might be placed in a cool zone that is
serviced by redundant AC units.
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Data center Green datacenters
Datacenters in arctic locations where
outside air provides all cooling are getting
more popular as cooling and electricity are
the two main variable cost components.
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Data center Network infrastructure
1
Communications in data centers today
are most often based on networks
running the IP protocol suite. Data
centers contain a set of routers and
switches that transport traffic between
the servers and to the outside world.
Redundancy of the Internet
connection is often provided by using
two or more upstream service
providers (see Multihoming).
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Data center Network infrastructure
1
Some of the servers at the data center
are used for running the basic Internet
and intranet services needed by
internal users in the organization, e.g.,
e-mail servers, proxy servers, and DNS
servers.
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Data center Network infrastructure
Network security elements are also
usually deployed: firewalls, VPN
gateways, intrusion detection systems,
etc. Also common are monitoring
systems for the network and some of
the applications. Additional off site
monitoring systems are also typical, in
case of a failure of communications
inside the data center.
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Data center Data center infrastructure management
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Data center infrastructure management
(DCIM) is the integration of information
technology (IT) and facility
management disciplines to centralize
monitoring, management and
intelligent capacity planning of a data
center's critical systems. Achieved
through the implementation of
specialized software, hardware and
sensors, DCIM enables common, realhttps://store.theartofservice.com/itil-2011-foundation-complete-certification-kit-fourth-edition-study-guide-ebook-and-online-course.html
Data center Data center infrastructure management
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Depending on the type of implementation,
DCIM products can help data center
managers identify and eliminate sources
of risk to increase availability of critical IT
systems. DCIM products also can be used
to identify interdependencies between
facility and IT infrastructures to alert the
facility manager to gaps in system
redundancy, and provide dynamic, holistic
benchmarks on power consumption and
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Data center Data center infrastructure management
Server, storage, and staff utilization
metrics can contribute to a more complete
view of an enterprise data center
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Data center Applications
The main purpose of a data center is
running the applications that handle the
core business and operational data of the
organization. Such systems may be
proprietary and developed internally by the
organization, or bought from enterprise
software vendors. Such common
applications are ERP and CRM systems.
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Data center Applications
1
A data center may be concerned with
just operations architecture or it may
provide other services as well.
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Data center Applications
1
Often these applications will be composed
of multiple hosts, each running a single
component. Common components of such
applications are databases, file servers,
application servers, middleware, and
various others.
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Data center Applications
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Encrypted backups can be sent over the
Internet to another data center where they
can be stored securely.
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Data center Applications
For quick deployment or disaster
recovery, several large hardware
vendors have developed mobile
solutions that can be installed and
made operational in very short time.
Companies such as Cisco Systems,
Sun Microsystems (Sun Modular
Datacenter), Bull, IBM (Portable
Modular Data Center), HP (Performance
Optimized Datacenter), and Google
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3G Data rates
While stating in commentary that "it is
expected that IMT-2000 will provide higher
transmission rates: a minimum data rate of
2 Mbit/s for stationary or walking users,
and 384 kbit/s in a moving vehicle," the
ITU does not actually clearly specify
minimum required rates, nor required
average rates, nor what modes of the
interfaces qualify as 3G, so various[vague]
data rates are sold as '3G' in the market
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BASIC Data types and variables
1
Minimal versions of BASIC had only
integer variables and one-letter
variable names. More powerful
versions had floating-point
arithmetic, and variables could be
labelled with names six or more
characters long.
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BASIC Data types and variables
String variables are usually
distinguished in many microcomputer
dialects by having $ suffixed to their
name, and string values are typically
enclosed in quotation marks.
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BASIC Data types and variables
1
Arrays in BASIC could
contain integers,
floating point or string
variables.
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BASIC Data types and variables
Some dialects of BASIC supported
matrices and matrix operations, useful for
the solution of sets of simultaneous linear
algebraic equations. These dialects would
support matrix operations such as
assignment, addition, multiplication (of
compatible matrix types), and evaluation
of a determinant. Microcomputer dialects
often lacked this data type and required a
programmer to provide subroutines to
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C data types
In the C programming language, data
types refers to an extensive system for
declaring variables of different types. The
language itself provides basic arithmetic
types and syntax to build array and
compound types. Several headers in the
standard library contain definitions of
support types, that have additional
properties, such as exact size,
guaranteed.
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C data types Basic types
The C language provides many basic
types. Most of them are formed from one
of the four basic arithmetic type specifiers
in C (char, int, float and double), and
optional specifiers (signed, unsigned,
short, long). All available basic arithmetic
types are listed below:
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C data types Basic types
char smallest addressable unit of the
machine that can contain basic character
set. It is an integer type. Actual type can
be either signed or unsigned depending on
the implementation.
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C data types Basic types
1
signed char same size
as char, but
guaranteed to be
signed.
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C data types Basic types
unsigned charsame size
as char, but guaranteed to
be unsigned.
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C data types Basic types
1
unsigned short int same as
short, but unsigned.
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C data types Basic types
1
unsigned long int
same as long, but
unsigned.
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C data types Basic types
1
unsigned long long int same as long long,
but unsigned. Specified only in C99
version of the standard.
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C data types Basic types
1
float (single precision) floating-point type.
Actual properties unspecified, however on
most systems this is IEEE 754 single
precision floating point format.
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C data types Basic types
1
double double precision floating-point
type. Actual properties unspecified,
however on most systems this is IEEE
754 double precision floating point
format.
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C data types Basic types
1
long double extended precision floatingpoint type. Actual properties unspecified.
Unlike types float and double, it can be
either 80-bit floating point format, the nonIEEE "double-double" or IEEE 754
quadruple precision floating-point format if
a higher precision format is provided,
otherwise it is the same as double. See
this page for details.
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C data types Basic types
1
The actual size of integer types varies by
implementation. The standard only
requires size relations between the data
types and minimum sizes for each data
type:
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C data types Basic types
1
The relation requirements are that the
long long is not smaller than long,
which is not smaller than int, which is
not smaller than short. As char's size
is always the minimum supported
data type, all other data types can't be
smaller.
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C data types Basic types
The minimum size for char is 8 bit, the
minimum size for short and int is 16 bit, for
long it is 32 bit and long long must contain
at least 64 bit.
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C data types Basic types
1
The type int should be the integer type that
the target processor is most efficient
working with. This allows great flexibility:
for example, all types can be 64-bit.
However, several different integer width
schemes (data models) are popular. This
is because the data model defines how
different programs communicate, a
uniform data model is used within a given
operating system application interface.
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C data types Basic types
1
In practice it should be noted that char
is usually 8 bits in size and short is
usually 16 bits in size (as are their
unsigned counterparts). This holds
true for platforms as diverse as 1990s
SunOS 4 Unix, Microsoft MS-DOS,
modern Linux, and Microchip MCC18
for embedded 8 bit PIC
microcontrollers. POSIX requires char
to be exactly 8 bits in size.
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C data types Basic types
1
The actual size of floating point types
also varies by implementation. The
only guarantee is that long double is
not smaller than double, which is not
smaller than float. Usually, 32-bit and
64-bit IEEE 754 floating point formats
are used, if supported by hardware.
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C data types Boolean type
1
C99 added a boolean (true/false) type
(_Bool) which is defined in the
<stdbool.h> header. Additionally, the
standard requires that macros are
defined to alias the type as bool as well
as providing macros for true and false.
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C data types Size and pointer difference types
The C language provides the separate
types size_t and ptrdiff_t to represent
memory-related quantities. Existing types
were deemed insufficient, because their
size is defined according to the target
processor's arithmetic capabilities, not the
memory capabilities, such as available
address space. Both of these types are
defined in the <stddef.h> header (cstddef
header in C++).
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C data types Size and pointer difference types
1
size_t is used to represent the size of any
object (including arrays) in the particular
implementation. It is used as the return
type of the sizeof operator. The maximum
size of size_t is provided via SIZE_MAX, a
macro constant which is defined in the
<stdint.h> header (cstdint header in C++).
It is guaranteed to be at least 65535.
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C data types Size and pointer difference types
Note that size_t is unsigned,
signed sizes can be represented
by ssize_t.
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C data types Interface to the properties of the basic types
1
Information about the actual properties,
such as size, of the basic arithmetic types,
is provided via macro constants in two
headers: <limits.h> header (climits header
in C++) defines macros for integer types
and <float.h> header (cfloat header in
C++) defines macros for floating-point
types. The actual values depend on the
implementation.
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C data types Interface to the properties of the basic types
1
SCHAR_MIN, SHRT_MIN, INT_MIN,
LONG_MIN, LLONG_MIN(C99) –
minimum possible value of signed
integer types: signed char, signed
short, signed int, signed long, signed
long long
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C data types Interface to the properties of the basic types
1
SCHAR_MAX, SHRT_MAX, INT_MAX,
LONG_MAX, LLONG_MAX(C99) –
maximum possible value of signed integer
types: signed char, signed short, signed
int, signed long, signed long long
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C data types Interface to the properties of the basic types
1
UCHAR_MAX, USHRT_MAX, UINT_MAX,
ULONG_MAX, ULLONG_MAX(C99) –
maximum possible value of unsigned
integer types: unsigned char, unsigned
short, unsigned int, unsigned long,
unsigned long long
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C data types Interface to the properties of the basic types
1
CHAR_MIN – minimum
possible value of char
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C data types Interface to the properties of the basic types
1
CHAR_MAX – maximum
possible value of char
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C data types Interface to the properties of the basic types
MB_LEN_MAX – maximum
number of bytes in a multibyte
character
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C data types Interface to the properties of the basic types
FLT_MIN, DBL_MIN, LDBL_MIN –
minimum normalized positive value of
float, double, long double respectively
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C data types Interface to the properties of the basic types
1
FLT_TRUE_MIN, DBL_TRUE_MIN,
LDBL_TRUE_MIN (C11) – minimum
positive value of float, double, long
double respectively
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C data types Interface to the properties of the basic types
1
FLT_MAX, DBL_MAX, LDBL_MAX – maximum finite
value of float, double, long double respectively
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C data types Interface to the properties of the basic types
1
FLT_ROUNDS – rounding mode for
floating-point operations
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C data types Interface to the properties of the basic types
1
FLT_EVAL_METHOD – evaluation method
of expressions involving different floatingpoint types (only available in C99)
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C data types Interface to the properties of the basic types
1
FLT_DIG, DBL_DIG, LDBL_DIG – number
of decimal digits that can be represented
without losing precision by float, double,
long double respectively
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C data types Interface to the properties of the basic types
FLT_EPSILON, DBL_EPSILON,
LDBL_EPSILON – difference between
1.0 and the next representable value of
float, double, long double respectively
1
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C data types Interface to the properties of the basic types
FLT_MANT_DIG, DBL_MANT_DIG,
LDBL_MANT_DIG – number of
FLT_RADIX-base digits in the
floating-point significand for types
float, double, long double respectively
1
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C data types Interface to the properties of the basic types
FLT_MIN_EXP, DBL_MIN_EXP,
LDBL_MIN_EXP – minimum negative
integer such that FLT_RADIX raised to a
power one less than that number is a
normalized float, double, long double
respectively
1
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C data types Interface to the properties of the basic types
1
FLT_MIN_10_EXP, DBL_MIN_10_EXP,
LDBL_MIN_10_EXP – minimum negative
integer such that 10 raised to a power one
less than that number is a normalized
float, double, long double respectively
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C data types Interface to the properties of the basic types
FLT_MAX_EXP, DBL_MAX_EXP,
LDBL_MAX_EXP – maximum positive
integer such that FLT_RADIX raised to a
power one more than that number is a
normalized float, double, long double
respectively
1
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C data types Interface to the properties of the basic types
1
FLT_MAX_10_EXP, DBL_MAX_10_EXP,
LDBL_MAX_10_EXP – maximum positive
integer such that 10 raised to a power one
more than that number is a normalized
float, double, long double respectively
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C data types Interface to the properties of the basic types
1
DECIMAL_DIG – minimum number of
decimal digits needed to represent all
the significant digits for long double.
The value is at least 10. (only available
in C99)
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C data types Fixed-width integer types
1
The C99 standard includes definitions
of several new integer types to
enhance the portability of programs
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C data types Fixed-width integer types
1
Exact-width integer types which are
guaranteed to have the same number
N of bits across all implementations.
Included only if it is available in the
implementation.
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C data types Fixed-width integer types
Least-width integer types which are
guaranteed to be the smallest type
available in the implementation, that has at
least specified number N of bits.
Guaranteed to be specified for at least
N=8,16,32,64.
1
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C data types Fixed-width integer types
Fastest integer types which are
guaranteed to be the fastest integer
type available in the implementation,
that has at least specified number N of
bits. Guaranteed to be specified for at
least N=8,16,32,64.
1
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C data types Fixed-width integer types
1
Pointer integer types which
are guaranteed to be able to
hold a pointer
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C data types Fixed-width integer types
1
Maximum-width integer types which are
guaranteed to be the largest integer type
in the implementation
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C data types Fixed-width integer types
1
The following table summarizes the types
and the interface to acquire the
implementation details (N refers to the
number of bits):
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C data types Fixed-width integer types
1
Type categorySigned
types Unsigned types
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C data types Fixed-width integer types
1
Pointer intptr_t INTPTR_MIN
INTPTR_MAX uintptr_t 0
UINTPTR_MAX
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C data types Printf and scanf format specifiers
The <inttypes.h> header (cinttypes
header in C++) provides features that
enhance the functionality of the types
defined in <stdint.h> header. Included
are macros that define printf format
string and scanf format string
specifiers corresponding to the
<stdint.h> types and several functions
for working with intmax_t and
uintmax_t types. This header was
1
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C data types Printf and scanf format specifiers
The macros are in the format
PRI{fmt}{type}. Here {fmt} defines the
output formatting and is one of d
(decimal), x (hexadecimal), o (octal), u
(unsigned) and i (integer). {type} defines
the type of the argument and is one of N,
FASTN, LEASTN, PTR, MAX, where N
corresponds to the number of bits in the
argument.
1
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C data types Printf and scanf format specifiers
The macros are in the format
SCN{fmt}{type}. Here {fmt} defines the
output formatting and is one of d
(decimal), x (hexadecimal), o (octal),
u (unsigned) and i (integer). {type}
defines the type of the argument and
is one of N, FASTN, LEASTN, PTR,
MAX, where N corresponds to the
number of bits in the argument.
1
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C data types Structures
1
Structures are a way of storing multiple
pieces of data in one variable. For
example, say we wanted to store the
name and birthday of a person in strings,
in one variable. We could use a structure
to house that data:
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C data types Structures
1
Structures may contain pointers to structs
of its own type, which is common in linked
datastructures.
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C data types Structures
A C implementation has freedom to
design the memory layout of the struct,
with few restrictions; one being that the
memory address of the first member
will be the same as the address of
struct itself. Structs may be initialized
or assigned to using compound literals.
1
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C data types Structures
1
A user-written function can directly return a
structure, though it will often not be very
efficient at run-time.
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C data types Arrays
1
For every type T, except void and function
types, there exist the types “array of N
elements of type T”.
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C data types Arrays
1
An array is a collection of values, all of the
same type, stored contiguously in memory.
An array of size N is indexed by integers
from 0 up to and including N-1.
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C data types Arrays
1
There are also "arrays of unspecified size"
where the number of elements is not
known by the compiler.
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C data types Arrays
int bob[]; // array of
an unspecified number
of 'int' elements.
1
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C data types Arrays
1
Arrays can be initialized with a compound
initializer, but not assigned. Arrays are
passed to functions by passing a pointer to
the first element.
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C data types Arrays
1
Multidimensional arrays are defined
as "array of array …". All but the
outermost dimension must have
compile-time constant size:
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C data types Arrays
1
float f[]; // array of unspecified
number of 'array of 32 float
elements'
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C data types Pointer types
Variables can be declared as being
pointers to values of various types, by
means of the * type declarator. To
declare a variable as a pointer,
precede its name with an asterisk.
1
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C data types Pointer types
1
Hence "for every type T" also applies to
pointer types there exists multi-indirect
pointers like char** or int*** and so on.
There exists also "pointer to array"
types, but they are less common than
"array of pointer", and their syntax is
quite confusing:
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C data types Pointer types
1
pc consumes 10×sizeof(char*) bytes
(usually 40 or 80 bytes on common
platforms), but pa is only one pointer,
so sizeof(pa) is usually 4 or 8, and the
data it refers to is an array of 10 bytes:
sizeof(*pa) == 10.
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C data types Unions
1
Union types are special structures which
allow access to the same memory using
different type descriptions; one could, for
example, describe a union of data types
which would allow reading the same data
as an integer, a float or a user declared
type:
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C data types Unions
1
In the above example the total size of u is
the size of u.s (which happens to be the
sum of the sizes of u.s.u and u.s.d), since
s is larger than both i and f. When
assigning something to u.i, some parts of
u.f may be preserved if u.i is smaller than
u.f.
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C data types Unions
1
Reading from a union member is not
the same as casting since the value of
the member is not converted, but
merely read.
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C data types Function pointers
Function pointers allow referencing
functions with a particular signature. For
example, to store the address of the
standard function abs in the variable
my_int_f:
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C data types Function pointers
1
int (*my_int_f)(int) = &abs; // the &
operator can be omitted, but makes
clear that the "address of" abs is used
here
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C data types Function pointers
Function pointers are invoked by
name just like normal function calls.
Function pointers are separate from
pointers and void pointers.
1
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Digital Enhanced Cordless Telecommunications DECT for data networks
The first DECT product to reach the
market, Olivetti's Net3, was a wireless
LAN, and German firms Dosch & Amand
and Hoeft & Wessel built niche businesses
on the supply of data transmission
systems based on DECT.
1
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Digital Enhanced Cordless Telecommunications DECT for data networks
1
had opened up to DECT, well into the new
century, the industry had moved far ahead
in terms of performance and DECT's time
as a technically competitive wireless data
transport had passed.
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C syntax Primitive data types
The C language represents numbers in
three forms: integral, real and complex.
This distinction reflects similar distinctions
in the instruction set architecture of most
central processing units. Integral data
types store numbers in the set of integers,
while real and complex numbers represent
numbers (or pair of numbers) in the set of
real numbers in floating point form.
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C syntax Primitive data types
1
All C have signed and unsigned
variants
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Amateur radio Text and data
Most amateur digital modes are
transmitted by inserting audio into the
microphone input of a radio and using an
analog scheme, such as amplitude
modulation (AM), frequency modulation
(FM), or single-sideband modulation
(SSB).
1
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Amateur radio Text and data
1
ALE Automatic Link
Establishment
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Amateur radio Text and data
1
AMateur Teleprinting
Over Radio (AMTOR)
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Amateur radio Text and data
1
Discrete multi-tone modulation
modes such as Multi Tone 63
(MT63)
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Amateur radio Text and data
Multiple Frequency-Shift
Keying (MFSK) modes such as
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Amateur radio Text and data
1
Automatic Packet
Reporting System
(APRS)
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C++/CX Metadata
All WinRT programs expose their
declared classes and members
through metadata. The format is the
same that was standardized as part of
the Common Language Infrastructure
(CLI), the standard created from the
.NET Framework. Because of this,
code can be shared across C++/CX,
CLI languages and Javascript that
target Windows Runtime.
1
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4G Data rate comparison
1
Name Family Primary Use
Radio Tech Downstream
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4G Data rate comparison
1
168 HSPA+ is widely deployed. Revision
11 of the 3GPP states that HSPA+ is
expected to have a throughput capacity of
672 Mbit/s.
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4G Data rate comparison
1
(in 20 MHz FDD) LTE-Advanced update
expected to offer peak rates up to 1 Gbit/s
fixed speeds and 100 Mb/s to mobile
users.
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4G Data rate comparison
1
376 (2x20 MHz FDD) Also, low mobility
users can aggregate multiple channels
to get a download throughput of up to 1
Gbit/s
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4G Data rate comparison
1
(11n) Mobile InternetOFDM/MIMO
288.8 (using 4x4 configuration in 20
MHz bandwidth) or 600 (using 4x4
configuration in 40 MHz bandwidth)
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4G Data rate comparison
1
Antenna, RF front end enhancements and
minor protocol timer tweaks have helped
deploy long range P2P networks
compromising on radial coverage,
throughput and/or spectra efficiency (310
km & 382 km)
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4G Data rate comparison
1
iBurst 802.20 Mobile InternetHCSDMA/TDD/MIMO 95 36 Cell
Radius: 3–12 km
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4G Data rate comparison
1
EDGE Evolution GSM
Mobile Internet
TDMA/FDD 1.6 0.5
3GPP Release 7
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4G Data rate comparison
1
5.76 HSDPA is widely deployed. Typical
downlink rates today 2 Mbit/s, ~200 kbit/s
uplink; HSPA+ downlink up to 56 Mbit/s.
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4G Data rate comparison
UMTS-TDD UMTS/3GSM Mobile
Internet CDMA/TDD 16Reported
speeds according to IPWireless using
16QAM modulation similar to
HSDPA+HSUPA
1
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4G Data rate comparison
1
1.8xN Rev B note: N is the number of
1.25 MHz chunks of spectrum used. EVDO is not designed for voice, and requires
a fallback to 1xRTT when a voice call is
placed or received.
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4G Data rate comparison
For more information, see
Comparison of wireless data
standards.
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IPv4 Data
1
The data portion of the packet is not
included in the packet checksum. Its
contents are interpreted based on the
value of the Protocol header field.
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IPv4 Data
In a typical IP implementation,
standard protocols such as TCP and
UDP are implemented in the OS
kernel, for performance reasons.
Other protocols such as ICMP may be
partially implemented by the kernel,
or implemented purely in user
software. Protocols not implemented
in-kernel, and not exposed by
standard APIs such as BSD sockets,
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IPv4 Data
See List of IP protocol
numbers for a complete
list.
1
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Array data type
In computer science, an array type is a
data type that is meant to describe a
collection of elements (values or
variables), each selected by one or more
indices (identifying keys) that can be
computed at run time by the program.
Such a collection is usually called an array
variable, array value, or simply array. By
analogy with the mathematical concepts of
vector and matrix, array types with one
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Array data type
For example, in the Pascal
programming language, the
declaration type MyTable = array
[1..4,1..2] of integer, defines a new
array data type called MyTable
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Array data type
1
Arrays are distinguished from lists in
that arrays allow random access,
while lists only allow sequential
access. Dynamic lists are also more
common and easier to implement
than dynamic arrays. Array types are
distinguished from record types
mainly because they allow the
element indices to be computed at run
time, as in the Pascal assignment
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Array data type
1
In more theoretical contexts, especially
in type theory and in the description of
abstract algorithms, the terms "array"
and "array type" sometimes refer to an
abstract data type (ADT) also called
abstract array or may refer to an
associative array, a mathematical
model with the basic operations and
behavior of a typical array type in most
languages — basically, a collection of
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Array data type
Depending on the language, array
types may overlap (or be identified
with) other data types that describe
aggregates of values, such as lists and
strings. Array types are often
implemented by array data structures,
but sometimes by other means, such
as hash tables, linked lists, or search
trees.
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Array data type History
Assembly languages and low-level
languages like BCPL generally have no
syntactic support for arrays.
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Array data type History
Because of the importance of array
structures for efficient computation, the
earliest high-level programming
languages, including FORTRAN (1957),
COBOL (1960), and Algol 60 (1960),
provided support for multi-dimensional
arrays.
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Array data type Abstract arrays
An array data structure can be
mathematically modeled as an
abstract data structure (an abstract
array) with two operations
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Array data type Abstract arrays
1
get(A, I): the data stored in the element of the array
A whose indices are the integer tuple I.
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Array data type Abstract arrays
1
set(A,I,V): the array that results by setting the value
of that element to V.
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Array data type Abstract arrays
1
These operations are
required to satisfy the
axioms
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Array data type Abstract arrays
for any array state A, any value V, and any
tuples I, J for which the operations are defined.
1
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Array data type Abstract arrays
The first axiom means that each
element behaves like a variable. The
second axiom means that elements
with distinct indices behave as
disjoint variables, so that storing a
value in one element does not affect
the value of any other element.
1
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Array data type Abstract arrays
1
These axioms do not place any constraints
on the set of valid index tuples I, therefore
this abstract model can be used for
triangular matrices and other oddlyshaped arrays.
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Array data type Implementations
In order to effectively implement
variables of such types as array
structures (with indexing done by
pointer arithmetic), many languages
restrict the indices to integer data types
(or other types that can be interpreted
as integers, such as bytes and
enumerated types), and require that all
elements have the same data type and
storage size
1
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Array data type Implementations
1
That is, those languages use array-like
syntax to implement a more general
associative array semantics, and must
therefore be implemented by a hash
table or some other search data
structure.
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Array data type Multi-dimensional arrays
1
The number of indices needed to specify
an element is called the dimension,
dimensionality, or rank of the array type
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Array data type Multi-dimensional arrays
1
Many languages support only
one-dimensional arrays
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Array data type Multi-dimensional arrays
This representation for multidimensional arrays is quite prevalent in
C and C++ software. However, C and
C++ will use a linear indexing formula
for multi-dimensional arrays that are
declared as such, e.g. by int A or int
A[m][n], instead of the traditional int
**A.:p.81
1
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Array data type Indexing notation
1
Most programming languages that support
arrays support the store and select
operations, and have special syntax for
indexing. Early languages used
parentheses, e.g. A(i,j), as in FORTRAN;
others choose square brackets, e.g. A[i,j]
or A[i][j], as in Algol 60 and Pascal.
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Array data type Index types
1
In some languages, however, array data
types have the semantics of associative
arrays, with indices of arbitrary type and
dynamic element creation
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Array data type Bounds checking
Some languages (like Pascal and
Modula) perform bounds checking on
every access, raising an exception or
aborting the program when any index
is out of its valid range. Compilers
may allow these checks to be turned
off to trade safety for speed. Other
languages (like FORTRAN and C)
trust the programmer and perform no
checks. Good compilers may also
1
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Array data type Index origin
1
Some languages, such as C, provide only
zero-based array types, for which the
minimum valid value for any index is 0.
This choice is convenient for array
implementation and address
computations. With a language such as C,
a pointer to the interior of any array can be
defined that will symbolically act as a
pseudo-array that accommodates negative
indices. This works only because C does
not check an index against bounds when
used.
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Array data type Index origin
Other languages provide only onebased array types, where each index
starts at 1; this is the traditional
convention in mathematics for matrices
and mathematical sequences. A few
languages, such as Pascal, support nbased array types, whose minimum
legal indices are chosen by the
programmer. The relative merits of
each choice have been the subject of
1
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Array data type Index origin
1
See comparison of programming languages (array)
for the base indices used by various languages.
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Array data type Highest index
The relation between numbers
appearing in an array declaration and
the index of that array's last element
also varies by language. In many
languages (such as C), one should
specify the number of elements
contained in the array; whereas in
others (such as Pascal and Visual
Basic .NET) one should specify the
numeric value of the index of the last
1
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Array data type Array algebra
1
Some programming languages (including
APL, Matlab, and newer versions of
Fortran) directly support array
programming, where operations and
functions defined for certain data types are
implicitly extended to arrays of elements of
those types. Thus one can write A+B to
add corresponding elements of two arrays
A and B. The multiplication operation may
be merely distributed over corresponding
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Array data type String types and arrays
1
Many languages provide a built-in string
data type, with specialized notation ("string
literals") to build values of that type. In
some languages (such as C), a string is
just an array of characters, or is handled in
much the same way. Other languages, like
Pascal, may provide vastly different
operations for strings and arrays.
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Array data type Array index range queries
1
Some programming languages provide
operations that return the size (number of
elements) of a vector, or, more generally,
range of each index of an array. In C and
C++ arrays do not support the size
function, so programmers often have to
declare separate variable to hold the size,
and pass it to procedures as a separate
parameter.
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Array data type Array index range queries
1
Elements of a newly created array may
have undefined values (as in C), or may
be defined to have a specific "default"
value such as 0 or a null pointer (as in
Java).
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Array data type Array index range queries
1
In C++ a std::vector object supports
the store, select, and append
operations with the performance
characteristics discussed above.
Vectors can be queried for their size
and can be resized. Slower operations
like inserting an element in the
middle are also supported.
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Array data type Slicing
An array slicing operation takes a
subset of the elements of an arraytyped entity (value or variable) and then
assembles them as another array-typed
entity, possibly with other indices
1
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Array data type Resizing
Some languages allow dynamic arrays
(also called resizable, growable, or
extensible): array variables whose index
ranges may be expanded at any time after
creation, without changing the values of its
current elements.
1
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Array data type Resizing
For one-dimensional arrays, this
facility may be provided as an
operation "append(A,x)" that
increases the size of the array A by
one and then sets the value of the last
element to x
1
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Array data type Resizing
1
An extensible array can be implemented
as a fixed-size array, with a counter that
records how many elements are actually in
use. The append operation merely
increments the counter; until the whole
array is used, when the append operation
may be defined to fail. This is an
implementation of a dynamic array with a
fixed capacity, as in the string type of
Pascal. Alternatively, the append operation
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Predictive analytics Technology and big data influences
1
Today, exploring big data and using
predictive analytics is within reach of
more organizations than ever before.
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Conventional PCI Data phases
1
After the address phase (specifically,
beginning with the cycle that
DEVSEL# goes low) comes a burst of
one or more data phases. In all cases,
the initiator drives active-low byte
select signals on the C/BE[3:0]# lines,
but the data on the AD[31:0] may be
driven by the initiator (in case of
writes) or target (in case of reads).
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Conventional PCI Data phases
1
During data phases, the C/BE[3:0]# lines are
interpreted as active-low byte enables
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Conventional PCI Data phases
1
A data phase with all four C/BE# lines
deasserted is explicitly permitted by
the PCI standard, and must have no
effect on the target other than to
advance the address in the burst
access in progress.
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Conventional PCI Data phases
The data phase continues until both
parties are ready to complete the transfer
and continue to the next data phase. The
initiator asserts IRDY# (initiator ready)
when it no longer needs to wait, while the
target asserts TRDY# (target ready).
Whichever side is providing the data must
drive it on the AD bus before asserting its
ready signal.
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Conventional PCI Data phases
Once one of the participants asserts its
ready signal, it may not become un-ready
or otherwise alter its control signals until
the end of the data phase. The data
recipient must latch the AD bus each cycle
until it sees both IRDY# and TRDY#
asserted, which marks the end of the
current data phase and indicates that the
just-latched data is the word to be
transferred.
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Conventional PCI Data phases
1
To maintain full burst speed, the data
sender then has half a clock cycle
after seeing both IRDY# and TRDY#
asserted to drive the next word onto
the AD bus.
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Conventional PCI Data phases
For clocks 8 and 9, both sides remain
ready to transfer data, and data is
transferred at the maximum possible rate
(32 bits per clock cycle).
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Conventional PCI Data phases
1
In case of a read, clock 2 is reserved
for turning around the AD bus, so the
target is not permitted to drive data on
the bus even if it is capable of fast
DEVSEL.
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C Sharp (programming language) Categories of data types
1
Instances of value types do not have
referential identity nor referential
comparison semantics - equality and
inequality comparisons for value
types compare the actual data values
within the instances, unless the
corresponding operators are
overloaded
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C Sharp (programming language) Categories of data types
1
In contrast, reference types have the
notion of referential identity - each
instance of a reference type is
inherently distinct from every other
instance, even if the data within both
instances is the same
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C Sharp (programming language) Categories of data types
1
Both type categories
are extensible with
user-defined types.
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Customer intelligence Example sources of data for CI
Speech analytics – used to monitor
telephone conversations taking place
between companies and customers, using
phonetic analysis or speech to text to find
keywords and phrases, classify call types
and identify trends.
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Customer intelligence Example sources of data for CI
Click Tracking – used to monitor the
popularity and usage of corporate web
sites, this data can provide clues to
product interest and buying intention. For
example, a company may infer a customer
is interested in purchasing a particular
service if they are spending time browsing
specific product pages.
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Customer intelligence Example sources of data for CI
1
Customer Relationship Management –
software solutions used for Salesforce
automation and to manage customer
relationships which can store data on the
quantity, type and category of customer
and prospect contacts.
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Customer intelligence Example sources of data for CI
1
Frontline data capture which may (or may
not) form part of a CRM software solution,
but which is used by front line agents to
record more subjective data regarding
customer contacts, such as the root cause
of the customer picking up the phone (e.g.
they received their bill) or their emotional
state.
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Customer intelligence Example sources of data for CI
Customer satisfaction and market
research surveys, often mined via text
analytics, which can additionally be
applied, for customer intelligence
purposes, to Contact Center notes, email, and other textual sources.
1
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C++11 Modification to the definition of plain old data
1
In C++03, a class or struct must follow a
number of rules in order for it to be
considered a plain old data (POD) type
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C++11 Modification to the definition of plain old data
C++11 relaxed several of the POD
rules, by dividing the POD concept
into two separate concepts: trivial and
standard-layout.
1
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C++11 Modification to the definition of plain old data
A type that is trivial can be statically
initialized. It also means that it is legal to
copy data around via memcpy, rather than
having to use a copy constructor. The
lifetime of a trivial type begins when its
storage is defined, not when a constructor
completes.
1
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C++11 Modification to the definition of plain old data
1
A trivial class or struct is defined
as one that:
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C++11 Modification to the definition of plain old data
1
Has a trivial default constructor. This
may use the default constructor
syntax (SomeConstructor() = default;).
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C++11 Modification to the definition of plain old data
1
Has trivial copy and
move constructors,
which may use the
default syntax.
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C++11 Modification to the definition of plain old data
1
Has trivial copy and move assignment operators,
which may use the default syntax.
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C++11 Modification to the definition of plain old data
1
Has a trivial destructor,
which must not be
virtual.
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C++11 Modification to the definition of plain old data
1
Constructors are trivial only if there
are no virtual member functions of the
class and no virtual base classes.
Copy/move operations also require
that all of the non-static data members
be trivial.
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C++11 Modification to the definition of plain old data
1
A type that is standard-layout means that it
orders and packs its members in a way
that is compatible with C. A class or struct
is standard-layout, by definition, provided:
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C++11 Modification to the definition of plain old data
1
It has no virtual base
classes
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C++11 Modification to the definition of plain old data
1
All its non-static data members have the same
access control (public, private, protected)
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C++11 Modification to the definition of plain old data
All its non-static data members,
including any in its base classes, are
in the same one class in the hierarchy
1
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C++11 Modification to the definition of plain old data
1
The above rules also apply to all the
base classes and to all non-static data
members in the class hierarchy
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C++11 Modification to the definition of plain old data
A class/struct/union is considered
POD if it is trivial, standard-layout,
and all of its non-static data members
and base classes are PODs.
1
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C++11 Modification to the definition of plain old data
1
By separating these concepts, it becomes
possible to give up one without losing the
other. A class with complex move and
copy constructors may not be trivial, but it
could be standard-layout and thus interop
with C. Similarly, a class with public and
private non-static data members would not
be standard-layout, but it could be trivial
and thus memcpy-able.
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Circuit switching Compared to datagram packet switching
1
Circuit switching contrasts with packet
switching which divides the data to be
transmitted into small units, called
packets, transmitted through the network
independently. Packet switching shares
available network bandwidth between
multiple communication sessions.
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Circuit switching Compared to datagram packet switching
Multiplexing multiple
telecommunications connections over
the same physical conductor has been
possible for a long time, but
nonetheless each channel on the
multiplexed link was either dedicated
to one call at a time, or it was idle
between calls.
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Circuit switching Compared to datagram packet switching
In circuit switching, and virtual
circuit switching, a route and
bandwidth is reserved from source to
destination. Circuit switching can be
relatively inefficient because capacity
is guaranteed on connections which
are set up but are not in continuous
use, but rather momentarily. However,
the connection is immediately
available while established.
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Circuit switching Compared to datagram packet switching
1
Datagram packet switching networks
do not require a circuit to be
established and allow many pairs of
nodes to communicate concurrently
over the same channel.
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Data mining
1
Aside from the raw analysis step, it
involves database and data
management aspects, data preprocessing, model and inference
considerations, interestingness
metrics, complexity considerations,
post-processing of discovered
structures, visualization, and online
updating.
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Data mining
1
Often the more general terms "(large
scale) data analysis", or "analytics" –
or when referring to actual methods,
Artificial Intelligence and machine
learning – are more appropriate.
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Data mining
Neither the data collection, data
preparation, nor result interpretation
and reporting are part of the data
mining step, but do belong to the
overall KDD process as additional
steps.
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Data mining
The related terms data dredging, data
fishing, and data snooping refer to the use
of data mining methods to sample parts of
a larger population data set that are (or
may be) too small for reliable statistical
inferences to be made about the validity of
any patterns discovered. These methods
can, however, be used in creating new
hypotheses to test against the larger data
populations.
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Data mining
1
Data mining interprets its data into
real time analysis that can be used to
increase sales, promote new product,
or delete product that is not valueadded to the company.
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Data mining Etymology
Currently, Data
Mining and
Knowledge
Discovery are used
interchangeably.
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Data mining Background
1
It bridges the gap from applied statistics
and Artificial Intelligence (which usually
provide the mathematical background) to
database management by exploiting the
way data is stored and indexed in
databases to execute the actual learning
and discovery algorithms more efficiently,
allowing such methods to be applied to
ever larger data sets.
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Data mining Research and evolution
1
The premier professional body in the
field is the Association for Computing
Machinery's (ACM) Special Interest
Group (SIG) on Knowledge Discovery
and Data Mining (SIGKDD). Since 1989
this ACM SIG has hosted an annual
international conference and published
its proceedings, and since 1999 it has
published a biannual academic journal
titled "SIGKDD Explorations".
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Data mining Research and evolution
Computer science
conferences on data mining
include:
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Data mining Research and evolution
1
DMKD Conference –
Research Issues on
Data Mining and
Knowledge
Discovery
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Data mining Research and evolution
ECDM Conference –
European Conference on
Data Mining
1
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Data mining Research and evolution
1
ECML-PKDD Conference – European
Conference on Machine Learning and
Principles and Practice of Knowledge
Discovery in Databases
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Data mining Research and evolution
1
EDM Conference – International
Conference on Educational Data
Mining
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Data mining Research and evolution
1
PAKDD Conference – The annual Pacific-Asia
Conference on Knowledge Discovery and Data
Mining
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Data mining Research and evolution
1
SSTD Symposium –
Symposium on
Spatial and Temporal
Databases
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Data mining Research and evolution
1
Data mining topics are also present on
many data management/database
conferences such as the ICDE
Conference, SIGMOD Conference and
International Conference on Very Large
Data Bases
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Data mining Process
1
(5) Interpretation/Evaluation.
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Data mining Process
1
It exists, however, in many variations
on this theme, such as the Cross
Industry Standard Process for Data
Mining (CRISP-DM) which defines six
phases:
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Data mining Process
1
(5) Evaluation
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Data mining Process
1
or a simplified process such as (1) , (2) data mining,
and (3) results validation.
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Data mining Process
1
Polls conducted in 2002, 2004, and 2007
show that the CRISP-DM methodology is
the leading methodology used by data
miners. The only other data mining
standard named in these polls was
SEMMA. However, 3-4 times as many
people reported using CRISP-DM. Several
teams of researchers have published
reviews of data mining process models,
and Azevedo and Santos conducted a
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Data mining Pre-processing
1
Data cleaning removes the observations containing
noise and those with missing data.
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Data mining
Anomaly detection
(Outlier/change/deviation detection)
– The identification of unusual data
records, that might be interesting or
data errors that require further
investigation.
1
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Data mining
Association rule learning
(Dependency modeling) – Searches for
relationships between variables. For
example a supermarket might gather
data on customer purchasing habits.
Using association rule learning, the
supermarket can determine which
products are frequently bought
together and use this information for
marketing purposes. This is
1
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Data mining
Clustering – is the task of discovering
groups and structures in the data that are
in some way or another "similar", without
using known structures in the data.
1
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Data mining
Classification – is the task of
generalizing known structure to apply
to new data. For example, an e-mail
program might attempt to classify an email as "legitimate" or as "spam".
1
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Data mining
1
Regression – Attempts to find a function which
models the data with the least error.
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Data mining
Summarization – providing a more
compact representation of the data
set, including visualization and report
generation.
1
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Data mining Results validation
For example, a data mining algorithm
trying to distinguish "spam" from
"legitimate" emails would be trained on a
training set of sample e-mails
1
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Data mining Results validation
1
If the learned patterns do not meet the
desired , subsequently it is necessary
to re-evaluate and change the preprocessing and data mining steps. If
the learned patterns do meet the
desired , then the final step is to
interpret the learned patterns and
turn them into knowledge.
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