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Computer Science
An Overview
EE461
Introduction to Computer Science
1
Preface
4 Beginning computer science students need
exposure to the breadth of the subject in
which they are planning to major.
4 A foundation from which they can
understand the relevance and
interrelationships of future courses.
2
Introduction
4 Computer science is the discipline that
seeks to build a scientific foundation for a
variety of topics.
4 Computer science provides the
underpinnings for today’s computer
applications as well as the foundations for
tomorrow’s applications.
3
The Study of Algorithms
4 An algorithm is a set of steps that defines
how a task is performed.
4 In the domain of computing machinery,
algorithms are represented as programs
within computers.
4 Algorithms + Data Structure -> Programs,
Programs -> Software <=> Hardware.
4
The Study of Algorithms
4 The study of algorithms began as a subject
in mathematics.
4 The major goal is to find a single set of
directions that described how any problem
of a particular type could be solved.
4 E.g., the long division algorithm and the
Euclidean algorithm.
5
The Euclidean algorithm for finding the greatest
common divisor of two positive integers
6
The Study of Algorithms
4 Machine Architecture -
. Data storage (Ch. 1)
. Data manipulation (Ch. 2)
4 Software . Operating systems and networks (Ch. 3)
. Algorithms (Ch. 4)
. Programming languages (Ch. 5)
. Software engineering (Ch. 6)
4 Data Organization . Data structures (Ch. 7)
. File structures (Ch. 8)
. Database structures (Ch. 9)
4 AI and Theory of Computation
7
The Development of Algorithmic
Machines
4 Abacus.[ Ancient Greek+ Roman Civiliz.]
4 Babbage’s difference engine.[1850]
4 Jacquard’s loom.[1801]
4 Herman Hollerith (holes in paper
cards).[1890]
4 Mark I at Harvard University.[1944]
4 ENIAC at U. of Pennsylvania.[After 1944]
8
Jacquard’s loom
9
The Mark I computer
10
The Evolution of Computers
4 First Generation [1946-54]
4 Technologies [ Vacuum tubes; acoustic
memories; CRT memories].
4 Hardware features [ Fixed-point arithmetic]
4 Software features [assembly language]
11
The Evolution of Computers
4 Second Generation [1955-64]
4 Technologies [ Discrete transistors; ferrite cores;
magnetic disks].
4 Hardware features [ Floating-point arithmetic;
index registers; IO processors].
4 Software features [ High-level languages;
subroutine libraries; batch monitors].
12
The Evolution of Computers
4 Third Generation [1965-74]
4 Technologies [ Integrated circuits ( SSI and MSI)]
4 Hardware features [ Microprogramming;
pipelining; cache memory]
4 Software features [ Multiprogramming;
multiprocessing; operating systems; virtual
memory].
13
The Evolution of Computers
4 Fourth Generation [ 1975-1990]
4 Technologies [ LSI circuits; semiconductor
memories]
4 Fifth Generation [ 1990- ]
4 Technologies [ VLSI circuits ]
14
The central role of algorithms in computer
science
15
The Evolution of Computer
Science
Languages
Software
Algorithms
Hardware
Applications
16
Abstraction and Other Issues
4 Abstraction - the distinction between the
external properties of a component and the
internal details of the component’s
construction.
4 Ethical issues.
4 Social issues.
4 Legal issues.
17
The hierarchy of abstraction in the hardware
of a typical personal
18
Part I: Machine Architecture
4 A major process in the development of a
science is the construction of theories that
are confirmed or rejected by
experimentation.
4 In some cases these theories lie dormant for
extended periods, waiting for technology to
develop to the point that they can be tested.
19
Ch. 1 Data Storage
4
4
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4
4
Storage of bits.
Main memory.
Mass storage.
Coding information for storage.
The binary system.
Storing integers.
Storing Fractions.
Communication errors.
20
Storage of bits
4 Boolean operations, e.g., AND, NOT, and
OR.
4 Gates are devices that produce the output
of a Boolean operation when given the
operation’s input values.
4 A flip-flop is a circuit that has one of two
output values (i.e., 0 or 1), the output will
flip or flop between two values under
control of external stimuli.
21
The Boolean operations AND, OR, and XOR
(exclusive or)
22
A pictorial representation of AND, OR, XOR, and
NOT gates as well as their
input and output
values (continued)
23
A pictorial representation of AND, OR, XOR, and
NOT gates as well as their input and output values
24
A simple flip-flop circuit
25
Setting the output of a flip-flop to 1
(continued)
26
Setting the output of a flip-flop to 1
(continued)
27
Setting the output of a flip-flop to 1
28
Another way of constructing a flipflop
29
Hexadecimal Notation
4 In the internal activities of a computer, we must
deal with strings of bits , some of which can be
quite long, it is called a stream.
4 Streams are difficult for human mind to
manipulate. [ 1011010100111011 ]
4 To simplify the representation of such bit patterns.
4 We use hexadecimal notation.
30
The hexadecimal coding system
31
Storage of Bits
4 A flip-flop is ideal for the storage of a bit
within a computer (on a single wafer or
chip). A flip-flop loses data when its power
is turned off.
4 Cores, a donut-shaped rings of magnetic
material, are obsolete today due to their size
and power requirements.
4 A magnetic or laser storage device is
commonly used when longevity is important.
32
4 Hexadecimal notation.
Main Memory
4 Cells - a typical cell size is 8 or called byte.
4 Address is used to identify individual cells
in a main memory.
4 Random access memory (RAM).
4 Read only memory (ROM).
4 Most significant bit (MSB) and least
significant bit (LSB).
33
Memory cells arranged by address
34
The organization of a byte-size
memory cell
35
Mass Storage
4 Secondary memory.
4 Storing large units of data (called files).
4 Mass storage systems are slow due to
mechanical motion requirement.
4 On-line Vs. off-line operations.
36
Mass Storage
4
4
4
4
Disk storage.
Compact disks and CD-ROM.
Tape storage.
Physical Vs. logical records.
37
HARD DISK
38
HARD DISK
4 Track: Circle on the disk
4 Sector: Each track is divided into arcs called
Sector.
4 Seek Time: The time required to move the
read/write heads from one track to another.[
m sec ].
39
HARD DISK
4 Latency Time: Half the time required for
the disk to make a complete rotation.[m sec]
4 Access Time: The sum of the seek time and
the latency time(rotational delay)[ m sec ].
4 Transfer Rate: The rate at which data can be
transferred to or from the disk
40
HARD DISK
4 Hard disk Rotational Speed:
4 [ 7000- 10000- 15000 ] RPM
4 Floppy disk Rotational Speed;
4 [ 300 ] RPM
4 Transfer Rate: HD
4
Floppy
MB/sec
KB/sec
41
CD storage format
42
A magnetic tape storage mechanism
43
Data in Mass Storage
4 File: Information is stored on mass storage systems in
large units called File.
4 Buffer: is a storage area used to hold data on a temporary
basis usually during the process of being transferred from
one device to anther.
4 Physical record: a block of data conforming to the physical
characteristics of a storage device.
4 Logical record: natural division determined by the
information represented such naturally occurring blocks of
data are called logical records.
44
Logical records versus physical
records on a disk
45
Coding Information for Storage
4 American Standard Code for Information
Interchange (ASCII) - 8-bit codes.
4 International Standards Organization (ISO)
- 16-bit codes.
4 Binary-decimal number conversion.
46
The message “Hello.” in ASCII
47
The base ten and binary systems
48
Decoding the binary representation
100101
49
An algorithm for finding the binary
representation of a positive integer
50
Applying the algorithm to obtain the
binary representation of thirteen
51
Representing Images.
4 Images representation can be classified into two
4
4
4
4
categories:
Bit map techniques:
An image is considered to be a collection of dots,
each of which is called a pixel .
Vector Techniques:
An image is presented as a collection of lines and
curves. [Provides a means of scaling ]
52
Representing Images
4 Bit map representation
4 A pixel can be black or white , represented by a bit
4 A pixel can be a color , represented by three byte [
RBG ].
4 A typical photograph consists of 1280 rows of
1024 pixels
4 Requires several megabytes of storage
4 Image compression is required
53
Images Standards
4 Graphical Interchange Format [ GIF ]:
4 Each pixel is represented by a single byte.
4 Joint Photographic Experts Group[ JPEG]
4 Motion Picture Experts Group [MPEG]
54
Representing Sound
4 The most generic method of encoding audio
information for computer storage is to sample the
amplitude of the sound wave at regular intervals
and record the series of values obtained.
4 Rate of Sampling
4 8000 samples per sec.
4 Musical CDs use 44100 samples per sec.
55
The sound wave represented by the
sequence 0, 1.5, 2.0, 1.5, 2.0, 3.0,
4.0, 3.0, 0
56
The Binary System
4 Binary addition.
4 Fractions in binary.
4 Radix point (same as decimal point in
decimal notation).
57
The binary addition facts
58
Decoding the binary representation
101.101
59
Two’s complement notation systems
60
Coding the value -6 in two’s
complement notation using four bits
61
Addition problems converted to
two’s complement notation
62
An excess eight conversion table
63
An excess notation system using bit
patterns of length three
64
Floating-point notation components
65
Floating-Point Notation
4 EX. 0 110 1011 This means that:
4 The sign bit is 0 , the exponent is 110 ,and
the mantissa is 1011. To decode the byte
Extract the mantissa and place a radix point
on its left side. [ .1011 ]
4 Extract the exponent field [110] and
decoded from the three-bit excess method
i.e. +2
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Floating-Point Notation
4 This means that the radix in our solution to the
right by two bits.( a negative exponent would
mean to move the radix to the left)
4 Which gives [10.11]
4 The sign bit is 0 so the value represent a positive
value. [ + 10.11 ]
4 Truncation Errors: Round-off errors meaning that
part of the value being stored is lost because the
mantissa field is not large enough.
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Coding the value 2 5/8
68
Storing Fractions
4 Floating-point notation.
4 Sign bit => Exponent => Mantissa.
4 Round-off errors.
69
DATA COMPRESSION
4 Run-length encoding:
4 Best result when data being compressed
consist of long sequences of the same value.
It is the process of replacing such sequences
of the same value
4 11111111110000000000111111111111111
by 10 ones 10 zeros 15 ones
70
DATA COPRESSION
4 Relative Encoding
4 The approach is to record the differences
between consecutive data blocks rather than
entire blocks. i.e. Each block is encoded in
terms of its relationship to the previous
block.
71
DATA COMPRESSION
4 Frequency-dependant Encoding
4 The length of the bit pattern used to
represent a data item is inversely related to
the frequency of the item’s use.
4 Ex.
Variable length codes
4
Huffman codes
72
DATA COMPRESSION
4 Adaptive dictionary encoding
4 Lempel-Ziv encoding
4 ABAABQB (5,4,A) (0,0,D) (8,6,B)
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Decompressing xyxxyzy (5, 4, x)
74
Communication Errors
4 How can you make sure the information
you receive is correct???
4 Coding techniques for error detection and
correction.
4 Parity bits.
4 Error-correcting codes.
75
The ASCII codes for the letters A
and F adjusted for odd parity
76
An error-correcting code
77
Decoding the pattern 010100
78
Ch. 2 Data Manipulation
4 The central processing unit.
4 The stored-program concept.
4 Program execution.
4 Other architectures.
4 Arithmetic/logic instructions.
4 Computer-peripheral communication.
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The Central Processing Unit
CPU
ALU
Regs.
Control
unit
Bus
Main
memory
80
Adding values stored in memory
81
The Central Processing Unit
4 General-purpose registers - temporary
holding places for data being manipulated
by the CPU.
4 Cache memory (memory hierarchy!).
4 Bus - CPU/memory interface.
4 Machine instructions - data transfer,
arithmetic/logic, and control.
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The Stored-Program Concept
4 In early computing, the program is built
into the control unit as a part of the
machine. The user rewires the control unit
to adapt different programs.
4 Instructions as bit patterns - a program and
data can be coded and stored in main
memory. A computer’s program can be
changed merely by changing the contents of
the computer’s memory instead of rewiring
the control unit.
83
The Stored-Program Concept
4 The main concept of the stored-program is
that both program and data are stored in
main memory instead of data were stored in
memory and programs were part of the
control unit.
4 Machine instructions consists two fields:
op-code and operand.
84
Dividing values stored in memory
85
The Stored-Program Concept
ALU
CPU
Control unit
Regs.
Instr. Reg.
Program counter
Bus Op-code
Address
00 Main
FF memory
operand
86
Program Execution
4 The machine cycle:
4 1. Fetch: retrieve the next instruction from
memory and then increment the program
counter.
4 2. Decode: decode the bit pattern in the
instruction register.
4 3. Execute: perform action requested by the
instruction in the instruction register.
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The machine cycle
88
Decoding the instruction B258
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Figure 2.10: The program stored in
main memory ready for execution
90
Figure 2.11:Performing the fetch
step of the machine cycle (continued
91
Other Architectures
4 The design of a machine’s language -
complex instruction set Vs. simple
instruction set.
4 CISC Vs. RISC.
4 CISC – micro program.
4 RISC - simple CPU design.
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Performing the fetch step of the
machine cycle
93
Other Architectures
4 Pipelining - the throughput concept.
4 Multiprocessor machines - parallel
processing.
4 SISD, SIMD, MIMD.
4 Load balancing problem in multiprocessor
machines.
4 Distributed systems.
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Rotating the bit pattern A3 one bit
to the right
95
Arithmetic/Logic Instructions
4 Logic operations - AND, OR, XOR, ….
4 Masking (AND operation) and bit map.
4 Rotation and shift operations - logic shift
and arithmetic shift (leave the sign bit
unchanged).
4 Arithmetic operations - add, subtract,…..
96
Computer-Peripheral
Communication
4 Controllers handle communication between
machine’s CPU and peripheral devices.
4 The controllers are often a stand-alone
small computer, each with its own memory
and CPU that performs a program to
convert messages and data back and forth
between machine and a peripheral device.
97
Computer-Peripheral
Communication
Peripheral device
Controller
CPU
Bus
Main memory
Controller
Peripheral device
98
A conceptual representation of
memory-mapped I/O
99
Computer-Peripheral
Communication
4 Direct memory access (DMA) - the ability
of controller which can access memory
directly.
4 Buffering - a buffer is any location where
one system leaves data to be picked up later
by another.
4 von Neumann bottleneck - central
communication bus problem.
100
Computer-Peripheral
Communication
Peripheral device
Memory-mapped I/O
Controller
Main
memory
CPU
Bus
101
Computer-Peripheral
Communication
4 Port - the block of addresses associated
4
4
4
4
4
4
with a controller.
Handshaking - the two-way communication
that takes place between devices.
Parallel and serial communications.
Bits per second (bps) and baud rate.
Data compression.
Huffman code.
Lempel-Ziv encoding.
102
Part II: Software
4 In part II, we focus on topics associated
4
4
4
4
with software. In particular, we will
investigate the discovery, representation,
and communication of algorithms.
Operating systems and networks.
Algorithms.
Programming languages.
Software engineering.
103
Ch. 3 Operating Systems and
Networks
4 The evolution of operating systems.
4 Operating system architecture.
4 Coordinating the machine’s activities.
4 Handling Competition among processes.
4 Networks.
4 Network protocols.
104
Operating Systems
4 Why needs an operating system?
4 Computer applications often require a single
machine to perform activities that may compete
with one another for the machine’s resources. It
requires a high degree of coordination to ensure
that unrelated activities do not interfere with one
another and that communication between related
activities is efficient and reliable.
4
What is an operating system? A software
system which handles such a coordination
105
task.
The evolution of Operating
Systems
4 Single-processor systems.
4 Batch processing - the execution of jobs
(programs) by collecting them in a single
batch, then executing them without further
interaction with the user.
4 A job queue (FIFO) and a job control
language (JCL).
4 The main drawback to batch processing is
no interaction between user and job.
106
Batch processing
107
Software classification
108
The Evolution of Operating
Systems
Interactive processing,
Real-time processing.
Time-sharing.
Multitasking - time-sharing for a single
user systems.
4 Multiprocessor systems - networks such as
internet.
4 Load balancing and scaling problems.
4
4
4
4
109
Interactive processing
110
Operating System Architecture
Software
Application
Utility
System
Operating
system
Shell
Kernel
111
Operating System Architecture
4 A machine’s software can be divided into
two categories: application software and
system software.
4 Application software - the programs for
performing tasks particular to the
machine’s utilization.
4 System software - performs tasks which are
common to computer systems in general.
112
Operating System Architecture
Software
Application
Utility
System
Operating
system
Shell
Kernel
113
The shell as an interface between
users
and the operating system
114
Operating System Architecture
4 System software can be divided into two
categories: operating-system software and
utility software.
4 Utility software consists of software units
that extend the capabilities of the operating
system. For example, the ability to format a
disk or software for communicating through
a modem over telephone lines.
115
Operating System Architecture
4 Shell - the portion of an operating system
that defines the interface between the
operating system and its users.
4 Graphical user interface (GUI).
4 Importance of uniformity in the humanmachine interface across a variety of
machines.
4 UNIX Vs. MS-DOS and Windows.
116
Operating System Architecture
4 Kernel - the internal part of an operating
system, which contains those software
components that perform the very basic
functions required by the computer
installation.
4 File manager - directory (folder) and path.
4 Device drivers.
4 Memory manager.
117
OPERATING SYSTEM TASKS
4 1- Processor Management
4 2- Memory and Storage Management
4 3- Device Management
4 4- Application Interface
4 5- User Interface
118
OPERATING SYSTEM TASKS
4 A- Processor Management
4 1- Ensuring that each process and application receives
enough of the processor’s time to function properly.
4 2- Using as many processor cycles for real work as is
possible.
119
OPERATING SYSTEM TASKS
4 B- Memory and Storage Management
4 1- Each process must have enough memory in which to
execute and it can neither run into the memory space of
another process nor be run into by another process.
4 2- The different types of memory in the system must be
used properly so that each process can run most effective.
120
OPERATIND SYSTEM TASKS
4 C- Device Management
4 The path between the OS and virtually all hardware not on
the mother board goes through a special program called a
driver. It’s function to communicate with the controllers to
carry out operations on the peripheral devices.
121
OPERATION SYSTEM TASKS
4 D- Application Interface
4 Just as drivers provide a way for applications to make use
of hardware sub systems without having to know every
detail of the hardware operation. [Application Program
Interfaces ] use functions of the computer and operating
system without having to directly keep track of all the
details.
122
OPERATING SYSTEM TASKS
4 E- User Interface ( UI )
4 User Interface brings structure to the interaction between a
user and the computer.
4 EX:
4 Graphical User Interface [GUI]
4 Window Manager
123
Operating System Architecture
4
4
4
4
4
Main memory Vs. virtual memory.
Pages.
Scheduler and dispatcher.
Booting (booting strapping).
Bootstrap - a short program placed in ROM
and this program is executed automatically
when the machine is turned on.
124
VIRTUAL MEMORY
4 The memory mangers will divide the required space into
units called Pages and store the contents of these Pages in
mass storage [ typical page size a few K Bytes ]. When
different pages are required in the main memory. The
memory manager would exchange them for pages that are
not required. This is called VIRTUAL MEMORY
125
OPERATING SYSTEM
4 In time sharing system
4 Scheduler: determines which activities are
to be considered for execution
4 Dispatcher: controls the allocation of time
slices to these activities.
126
BOOTSTRAP
127
Coordinating the Machine
Activities
4 Process - is a dynamic activity whose
properties change as time progresses.
4 Process state - is a snapshot of the machine
at that time. For example, the current
position in the program being executed and
the values in the CPU registers.
4 A program Vs. a process.
4 Interprocess communication.
128
Coordinating the Machine’s
Activities
4 Process administration - the tasks
associated with process coordination are
handled by the scheduler and dispatcher
within the operating system’s kernel.
4 Process table - keeps information of a
process when it is created (assigned
memory area, the priority, the status - ready
or waiting).
129
between clients and servers
operating on the same machine and
distributed among different
machines
130
Coordinating the Machine’s
Activities
4 The dispatcher is the component of the
kernel that ensures that the scheduled
processes are actually executed.
4 In a time-sharing system, the dispatcher
divides time into time slices or quantum.
4 The dispatcher interrupts the process
running out of a time slice and assign a time
slice to another process (process switch).
131
Time-sharing between process A and
process B
132
Coordinating the Machine’s
Activities
4 The client/server model.
4 A client - makes requests of other units.
4 A server - satisfies the requests made by
clients.
4 The client/server model in the design
software leads to uniformity among the
types of communication taking place in the
system.
133
The client/server model
134
Handling Competition Among
Processes
4
4
4
4
Competing resources among processes.
Semaphores.
Test-and-set.
Critical region - is a sequence of
instructions which can be executed by only
one process.
135
DEADLOCK
136
Handling Competition Among
Processes
4 Deadlock - when two or more processes are
blocked from processing because each is
waiting for access to resources allocated to
another.
4 Three necessary conditions to avoid
deadlock:
4 1. There is competition for non-shareable
resources.
137
Handling Competition Among
Processes
4 2. The resources are requested on a partial
basis; that is, having received some
resources, a process will return later to
request more.
4 3. Once a resource has been allocated, it
cannot be forcibly retrieved.
4 Spooling - holding data for output at a later
but more convenient time.
138
Networks
4 Local area networks (LAN).
4 Wide area networks (WAN).
4 Proprietary networks.
4 Open networks.
4 Network topology - ring, bus, star, and
irregular.
139
140
141
NETWORK TOPOLOGIES
142
NETWORK TOPOLOGIES
143
The distinction between a bridge
and a router
144
Networks
4 Internet - initiated in 1973 by the Defense
Advanced Research Projects Agency
(DARPA). Goal: develop the ability to
connect a variety of computer networks o
that they can function as a single network.
4 Internet addressing - domains (a collection
of network clusters), network identifier,
host address; ex.,
[email protected].
145
Networks
4 Email and name server.
4 The world wide web - hypertext and
hypermedia documents.
4 A browser - a client.
4 Uniform resource locator (URL) - a
browser can contact the proper server and
request the desired document.
4 Hypertext Markup Language (HTML).
146
A typical approach to connecting
to the Internet
147
INTERNET ADDRESSING
Each machine in the internet is assigned a unique address
called IP
IP is a pattern of 32 bits consisting of two parts:
1- Pattern identifying the Domain ( Network Identifier )
2- Pattern identifying the machine within the domain
( Host Address ).
192.207.177.133
Network
Host
Identifier
Machine
Domain Name: ksu.edu.sa
148
INTERNET
Electronic Mail [ Mail Server ]
The world wide web [ www ]
The internet became a means of propagating multimedia
documents known as hypertext [ text, images, sound, and
video ]
Browser software is needed to browse the net.
In order to locate and retrieve documents on the ( www )
each document is given a unique address called Uniform
Resource Locator ( URL ).
Hypertext Markup Language [ HTML ]
149
A typical URL
150
A simple Web page expressed in
HTML
151
Network Protocols
Protocols - the rules that govern the
communication between different
components within a computer system.
Token ring protocol for networks with the
ring topology.
CSMA/CD (carrier sense, multiple access
with collision detection) in an Ethernet.
152
Communication over a ring network
153
Communication over a bus network
154
The Layered Approach to
Internet Software
This can be explained by the analogy if you were
to send a gift in a package from the west coast of
Saudi Arabia to a friend on the East coast.
You would warp the gift in a package and write
the address outside the package.
You would take the package to a shipping
company.
The following fig. shows the process for the
package-shipping example.
155
Package-shipping example
156
The internet software layers.
The internet software has four layers each
consisting of a collection of software
routines.
The four layers are known as the
application, transport, network, and link
layers.
Layers are present on each machine in the
internet.
157
The Internet software layers
158
NETWORK PROTOCOLS
1- The application layer
A- File Transfer Protocol [ FTP ]
B- Telnet: allowing a person to access a machine across
the internet.
C- Simple Mail Transfer Protocol ( SMTP ): Software used
by the mail servers when transferring E- mail.
159
NETWORK PROTOCOL
2- The transport layer
Its function is to accept messages from the application
layer and to ensure that the messages are properly
formatted for transmission over the internet.
160
NETWORK PROTOCOLS
3- The network layer
It is responsible for seeing that the packets it receives are
forwarded from one network within the internet to another
until they reach their final destinations
161
NETWORK PROTOCOLS
4- The link layer
Its function is to deal with the communication details
particularly to the individual network in which the machine
resides
162
Network Protocols: The Internet
Software Layer
Message source
Message destination
Application layer
Application layer
Transport layer
Transport layer
Network layer
Network layer
Link layer
Link layer
163
Network Protocols
Open system interconnection (OSI).
International standards organization (ISO).
TCP/IP (transmission control
protocol/internet protocol).
UDP (user datagram protocol).
164
Differences between TCP &UDP
1- TCP transport layer is said to establish a connection
before sending a message.
UDP dose not establish such a connection prior to sending
a message. UDP is called connectionless protocol.
2- TCP transport layers at the origin and destination work
together by means of acknowledgments and packet
retransmissions to confirm that all segments of a message
are successfully transferred to the destination.
UDP dose not offer such retransmission services but UDP
is more streamlined than TCP.
165
TCP AND UDP
E-mail is normally sent by TCP but the
communication carried out by the name
servers when translating addresses from
mnemonic form into IP form uses UDP.
IP is the internet standard for network layer.
166
Networks
Unauthorized access to information and
vandalism
Passwords and data encryption
Virus
Worm
167
SECURITY
1-Password
2- Public-key encryption
3- Secure Socket Layer [ SSL ]
168
VIRUS
It is a program segment that attaches itself
to other programs in the computer system.
When programs are executed, the virus may
perform malicious acts that are readly
noticeable.
169
WORM
Normally refers to an autonomous program
that transfer it self through a network,
taking up residence in machines and
forwarding copies of it self through the
network. These programs can be designed
merely to replicate themselves or to perform
additional vandalism
170
FIREWALL
It forms a protective barrier that shields the
region on one side from the dangers on the
other side.
171
Ch. 4 Algorithms
The concept of an algorithm.
Algorithm representation.
Algorithm discovery.
Iterative structures.
Recursive structures.
Efficiency and correctness.
172
The Concept of an Algorithm
An algorithm is an ordered set of
unambiguous, executable steps, defining a
terminating process.
Parallel algorithms.
Program Vs. algorithm Vs. process.
A program is a representation of an
algorithm
A process is the activity of executing an
algorihm.
173
ABSTRACT NATURE OF
ALGORITHM
An algorithm is abstract and distinct from its
representation. A single algorithm can be represented in
many ways.
EX: The algorithm for converting temperature readings
from Celsius to Fahrenheit.
F= (9/5 ) C + 32
Or it could be represented by
Multiply the Temp. Reading in C by 9/5 and then add 32 to
the product.
Or it can be represented by an electronic circuit [ Analogue
Computer].
174
ALGORITHM REPRESENTATION
The representation of an algorithm requires some form of
language. [ English, Arabic, Russian,....... ] or the language
of pictures.
Algorithm representation can be constructed, such a
building block is called a PRIMITIVE.
A collection of primitives along with a collection of rules
stating how primitives can be combined to represent more
complex ideas constitutes a Programming Language.
175
ALGORITHM REPRESENTATION
PRIMITIVE = SYNTAX + SEMANTICS
Syntax refers to the Primitive’s symbolic representation
Semantics refers to the meaning of the primitive.
AIR The syntax of AIR consists of the three symbols
A,I,R
The Semantics : AIR is gaseous substance that surrounds
the world.
176
Algorithm Representation
Primitive is a set of well-defined building
blocks which algorithm representations can
be constructed.
Primitive - graphical and texture.
Primitive => programming language.
Primitive - syntax and semantics.
177
Folding a bird from a square piece of
paper (continued)
178
Folding a bird from a square piece of
paper
179
Origami primitives (continued)
180
Origami primitives
181
Algorithm Representation
Pseudo code - is a notational system in
which ideas can be expressed informally
during the algorithm development process.
Ex. If you have more than $10 buy a cake;
otherwise buy nothing =>
if (cond) then (act1) else (act2)
Ex. As long as you have money, you an
spend => while(having money) do (spend)
182
Algorithm Representation
Ex. Assign name the value price+tax.
Begin a pseudocode with procedure name.
Ex. The pseudocode for Greetings:
procedure Greetings
assign Count the value 3;
while Count > 0 do
(print the message “Hello” and
assign Count the value Count - 1)
183
Algorithm Discovery
The development of a program consists of
two activities - discovering the underlying
algorithm and representing that algorithm as
a program.
The basic principles for problem-solving:
1. Understand the problem.
2. Get an idea as to how an algorithmic
procedure might solve the problem.
184
Algorithm Discovery
3. Formulate the algorithm and represent it
as a program.
4. Evaluate the program for accuracy and
for its potential as a tool for solving other
problems.
Conscious work Vs. inspiration.
Stepwise refinement - a top-down
methodology.
185
EXAMPLE
Person A is charged with the task of determining
the age of Person B’s 3 children:
CLUES:
1-The product of the children’s ages is 36
2- The sum of the children’s ages is given
Find the ages of the three children.
186
EXAMPLE
187
EXAMPLE
The clues is not enough because if the sum
is 13 we have two possibility 1+6+6 and
2+2+9.
We need a third clue.
The third clue is the oldest child plays the
piano
The solution is 2,2,9
188
EXAMPLE 2
Before A , B, C, and D ran a race they made the following
predictions:
A predicted that B would win
B predicted that D would be last
C predicted that A would be third
D predicted that A’s prediction would be correct
Only one of these predictions was true and this was the
prediction made by the winner. In which order did A , B ,
C , and D finish the race?
189
EXAMPLE 2
Prediction A & D are equivalent
Since only one Pred. Was true .Then A & D must be false.
Thus neither A nor D were winners.
If A Pred. Was false , the B did not win either.
The only remaining choice for winner is C. Thus C won
the race & C prediction was true.
If A came in third
We have two order for finishing the race
CBAD
OR
CDAB
190
EXAMPLE 2
C B A D OR
CDAB
CBAD order is ruled out because B’s
prediction must be false.
There fore finishing order was :
C D A B
191
THE SEQUENTIAL SEARCH
ALGORRITHM
The problem of searching sorted list for
Target value sequentially can be done by
comparing the target value to each entry in
the list.
The sequential search algorithm can be
presented in pseudo code as follows.
192
The sequential search algorithm in
pseudocode
193
Iterative Structures
Iterative structures - a collection of
instructions is repeated in a looping manner.
The while loop structure.
The repeat loop structure.
The insertion sort algorithm.
194
LOOP CONTROL
WHILE ( condition ) DO ( body )
{WHILE [ The pH level is greater than 4]
DO [ add a drop of sulphuric acid ]} add 3
times
No Termination Condition
Number 1 ;
WHILE ( Number NEQ 6 ) DO
( Number = Number + 2 )
195
Components of repetitive control
196
The while loop structure
197
REPEAT LOOP
REPEAT ( ACTIVITY ) UNTIL ( Condition)
EX:
Repeat ( take a coin from your pocket ) Until ( there are no
coins in your pocket ).
In the above ex. We assume there is a coin in your pocket
at the beginning, but
While ( there is a coin in your pocket ) Do ( take a coin
from your pocket )
We does not assume that.
198
The repeat loop structure
199
THE INSERTION SORT ALGORITHM
The insertion sort algorithm is useful for
sorting a list of names alphabetically. The
method selects a pivot entry and move it to
a temporary location and makes a
comparison with the list entries.
200
Sorting the list Fred, Alice, David,
Bill, and Carol alphabetically
(continued)
201
Sorting the list Fred, Alice, David,
Bill, and Carol alphabetically
(continued)
202
Sorting the list Fred, Alice, David,
Bill, and Carol alphabetically
203
The insertion sort algorithm
expressed in pseudocode
204
Recursive Structures
Recursive structures provide an alternative
to the loop paradigm for repetitive
structures (by invoking itself).
The binary search algorithm.
It is more faster than the sequential search
algorithm
205
Applying our strategy to search a list
for the entry John
206
A first draft of the binary search
technique
207
The binary search algorithm in
pseudocode
208
Binary Search Algorithm
Consider the list [ Alice , Bill , Carol , David , Evelyn ,
Fred , and George ], for the target value Bill. Our search
begins by selecting David ( the middle entry ) as the test
entry under consideration . Since the target value ( Bill ) is
less than this test entry, we are instructed to apply the
procedure Search to the list of entries preceding David
209
Binary Search Algorithm
210
Binary Search Algorithm
Let us consider the list:
[ Alice , Carol , Evelyn , Fred , and George ]
Searching for the entry ( David )
211
Binary Search Algorithm
212
Binary Search Algorithm
213
Binary Search Algorithm
214
Efficiency and Correctness
You can develop a variety of algorithms to
solve the same problem. However, the
choice between efficient and inefficient
algorithms can make the difference between
a practical solution to a problem and an
impractical one.
Time and storage complexity of the
algorithm.
215
Efficiency and Correctness
In the insertion sort algorithm the worst scenario is that
each pivot must be compared to all the preceding entries
before its proper location can be found.
This occurs if the original list is in reverse order. The first
pivot is compared to two names.
The total number of comparisons when sorting a list of n
entries is { 1+2+3+ -----+( n-1 ) } = ½[n(n-1]
If n=10 would require 45 comparison
216
Efficiency and Correctness
In the average case of insertion sort the
result is half worst case i.e.
¼[ n(n-1)] Comparison
217
Applying the insertion sort in a
worst-case situation
218
Graph of the worst-case analysis of
the insertion sort algorithm
219
Graph of the worst-case analysis of
the binary search algorithm
220
Efficiency and Correctness
How to make sure the algorithm and
program developed is correct?
Difference between testing and
verification.
Precondition, assertions, loop invariant.
221
SOFTWARE VERIFICATION
A traveller with a gold chain of seven links
must stay in an isolated hotel for seven
nights. The rent each night consists of one
link from the chain. What is the FEWEST
number of links that must be cut so that the
traveller can pay the hotel one link of the
chain each morning without paying for
lodging in advance.
222
Separating the chain using only
three cuts
223
Solving the problem with only one
cut
224
ALGORITHM
1- First morning give the hotel the single
link.
2- Second morning retrieve the single link
and give the hotel the two link piece.
3- Third morning Give the hotel the single
link.
4- Fourth morning retrieve the three links
held by the hotel and give the hotel the four
225
link piece.
ALGORITHM
5- Fifth morning give the hotel the single
link.
6- Sixth morning retrieve the single link and
give the hotel the double link piece.
7- Seventh morning give the hotel the single
link.
226
VERIFICATION
Proof of correctness begins with the
assumption that certain conditions, called
preconditions, are satisfied at the beginning
of the program’s execution.
How the consequences of these
preconditions propagate through the
program.
227
VERIFICATION
EX: IF ( condition) THEN ( instruction 1)
ELSE ( instruction 2)
If some statement is known to hold before
execution instruction 1, we know that both
that statement and the condition tested are
true. Where as if instruction 2 is to be
executed, we know the statement and the
negative of the condition must hold.
228
The assertions associated with a
typical while structure
229
Linear Programming
Linear programming is useful tool for
solving problems of allocation particularly
in maximising or minimising a linear
function of variables,
230
LP Example
A man owns large premises and wishes to
act as a retailer for one of the motor car
companies. His capital is 360000 SR and
his premises are large enough to
accommodate up to 36 cars. He chooses to
concentrate his sales on the rapidly selling
Maxi model and Mini model and decides to
order these two types only.
231
LP Example
The manufacturer supplies the Maxi and
Mini at 12000SR and 8000 SR respectively
and the man wishes to fix his profit on each
model at only 300SR and 240SR
respectively. By keeping his profits down,
he may hope to establish a reputation for
not over charging his customers, this is a
long-term objective.
232
LP Example
Arrangements have been made to prepare
the premises to receive the new cars directly
from the manufacturer. The man now has to
make a very important decision. How many
of each model should be order?
233
LP Graphical Solution
Let x be the number of Maxi cars to be
order.
Let y be the number of Mini cars to be order
Let P be the total profit he will make by
selling all his cars.
P is called Object function
234
LP Graphical Solution
The aim is to Maximize the objective
function
P = 300 x + 240 y
The restrictions of space and capital must
also be taken into account.
Such restrictions are called constraints.
235
LP Graphical Solution
x + y <= 36 Space Constraint
12000x+8000y<=360000 Capital
Constraint .
Dividing Capital Const. By 4000
3 x + 2 y <= 90
The LP problem can be stated as:
236
LP Solution
Max P = 300 x + 240 y
Subject to
x + y <= 36
3 x + 2 y <= 90
x >= 0
y >= 0
237
Graphical Solution
238
Simplex Method
1- Construct the first table
2- Locate a pivot
3- Calculate a new table
4- Repeat step 2 and 3 until the terminal table is obtained
239
Linear Programming Problem
The general form of the LP problem is as follows:
Max. the objective function
P = C1X1+ C2X2+ ----------- +CnXn
Subject To:
a11X1+a12X2+---------- +a1nXn <=b1
a21X1+a22X2+-----------+a2nXn <=b2
ak1X1+ak2X2+------------ +aknXn<=bk
X1,X2,------------- Xn >= 0
240
Simplex Method
1- After construction of the first table select any column
which contains the most negative coefficient for the non
basic variables.
2- Divide each positive entry in this column into the
corresponding element in the last column i.e. Form the
quotient ( b/a ) for all k pairs of elements unless a is
negative. The entry which yields the smallest quotient is
called the pivot. It’s column is called Pivotal Column and
its row is called Pivotal Row.
3- If the table has no negative indicator then it is called
Terminal table and has no pivot
241
Simplex Method
Consider the LP problem
Max.
P= 4*x1 + 3* x2
Subject to : 2x1 + 3x2 <= 6
- 3x1 + 2x2 <= 3
2x2 <= 5
2x1 + x2 <= 4
x1>=0 ,
x2 >= 0
The equations can be written as follows by introducing
Slack variable.
242
Simplex Method
P = 4x1+ 3x2+ 0S1+ 0S2+ 0S3 + 0S4
Subject to:
2x1+ 3x2+ S1
=6
-3x1+2x2
+S2
=3
0x1 + 2x2
+S3
=5
2x1 + x2
+S4 = 4
x1, x2, S1, S2, S3, S4 >= 0
The basic feasible solution x1= x2= 0 Then S1= 6 , S2= 3,
S3= 5 , and S4 =4.
243
Simplex Method ( First Table )
Basic P x1 x2 S1 S2 S3 S4 Solution
---------------------------------------------------------------------P0
0 -4
-3
0 0 0
0
0 P equation
----------------------------------------------------------------------S1
0 2
3
1 0 0
0
6
S2
0 -3
2
0 1 0
0
3
S3
0 0
2
0 0 1
0
5
S4 0 2
1
0 0 0
1 4
244
Simplex Method
The non basic variables are x1&x2= 0 yield P =0
The entering variable be selected as the non basic variable
having a negative coefficient in the P equation of the table.
In our example both x1 &x2 have negative coefficient .
The variable with the most negative coefficient is selected.
This column is called the pivotal column
245
Simplex Method
Basic
Solution
x1
Ratio(b/a)
S1
6
2
6/2=3
S2
3
-3
----S3
5
0
-----S4
4
2
4/2= 2 min.
2 is called the Pivot element.
Do row operation to get new table
We divide the row by the pivot and replace S4 by x1.
246
Simplex Method
The pivotal Row is divided by the pivot [ 2 ]
P0 x1 x2
S1 S2 S3 S4 Solution
X1 0 1 1/2
0
0
0
1/2
2
Do row operation for the other rows
P0
-4
-3
0
0
0
0
0
+
4
2
0
0
0
2
8
------------------------------------------------------------0
-1
0
0
0 2
8
247
Simplex Method
S1
2
3 1 0 0 0
6
+
-2
-1 0
0 0
-1
-4
------------------------------------------------------------0
2 1
0 0
-1
2
S2
-3
2 0
1 0
0
3
+
3
3/2 0
0 0
3/2
6
------------------------------------------------------------0
7/2 0 1 0
3/2
9
S3
0
2 0
0 1
0
5
248
Simplex Method ( New Table)
Basic P
P0
1
S1
0
S2
0
S3
0
X1
0
x1
0
0
0
0
1
x2
-1
2
7/2
2
1/2
S1 S2 S3 S4 Solution
0
0
0
2
8
1
0
0
-1
2
0
1
0 3/2
9
0
0
1
0
5
0
0
0 1/2
2
The new basic solution: x1= 2, x2= 0, S1= 2, S2= 9, S3=5,
S4= 0, and P=8
249
Simplex Method
The new entry variable is x2, it has the only negative
coefficient in P equation. To find the pivot do
Basic
Solution
x2
Ratio
S1
2
2
2/2=1 min. Pivot
S2
9
7/2
9(2/7)= 18/7
S3
5
2
5/2= 2.5
X1
2
1/ 2
2(2/1)=4
X2 is the entry variable and S1 is the leaving variable.
Do Row operation to get new table
250
Simplex Method ( New Table)
Basic P0 x1 x2 S1 S2 S3 S4 Solution
-----------------------------------------------------------------P0
1
0 0
1/ 2 0
0
3/2
9
-----------------------------------------------------------------X2
0
0 1
1/ 2 0
0
- 1/ 2
1
S2
0
0 0
-7/ 4 1
0
13/4
3/2
S3
0
0 0
-1
0
1
1
3
X1
0 1 0 - 1/ 4 0 0
3/ 4
3/ 2
Since all the coefficient of P equation is positive So we
have terminal table. The solution is :
X1= 1.5
X2= 1
P ( max )=9
251
LP Example
A paper company received three orders for paper rolls with
the widths and lengths indicated in the following table
Order Number
Width[m]
Length[m]
1
5
10000
2
7
30000
3
9
20000
Rolls are produced in the company in two standard
widths, 10 and 20 meters. There is no limit on the lengths
of the standard rolls. The objective is to determine the
production schedule ( cutting patterns) that minimizes the
trim losses while satisfying the given demand.
252
LP Example
<- ---- 10m-----> < ------- 20 m------------- >
______________
_____________________
< 5m > < 5m > < 7m > < 9m > < 4m >
Trim loss=0m
Trim loss = 4 m
For the above cut the trim loss for 10m is 0 m square but
the trim loss area for the 20 m roll is :
4*30000 + 9*10000 = 210000 m square
This is only one of the possible cutting patterns. The
optimum solution must specify the length of the standard
roll that must be cut according to each pattern.
253
LP Example
Now let Xij be the length of the i roll [ i=1 for 10m roll
and i=2 for 20m roll ] which is cut according to the jth
pattern.
Width X11 X12 X13
X21 X22 X23 X24 X25 X26 Requirements
5m
2
0
0
4
2
2
1
0
0
10000
7m
0
1
0
0
1
0
2
1
0
30000
9m
0
0
1
0
0
1
0
1
2
20000
___________________________________________________________________
Trim loss 0
3
1
0
3
1
1
4
2
254
LP Example
Now let S1, S2, and S3 be the surplus lengths produced of
the rolls with widths 5m, 7m, and 9m. So the LP problem
can be formulated as:
Minimize The objective function:
P= 3X12+X13+3X22+X24+4X25+2X25+5S1+7S2+9S3
Subject to:
2X11+4X21+2X22+2X23+X24- S1 = 10000
X12 +X22+ 2X24+ X25
-S2 = 30000
X13+X23+ X25+ 2X26
- S3 = 20000
Xij>=0 , Si >= 0 for all i and all j
255
Iterative Techniques
A) Repeated Substitution
Consider the two linear equations:
Y=7–X
[1]
X= ½ ( Y + 2 )
[2]
Solving it by the method of Repeated Substitution. Initially
let X= 0 Then substitute in Equation [1] to give Y=7
Then substitute in equation [2] to give X= 9/2
Then this value substitute in Eq. [1] gives new value of Y,
and this cycle process can be repeated indefinitely while
the value of X and Y approach the exact solution.
256
Graphical Solution ( X=3, Y=4)
257
Algorithm
1- Read Eqns.[1] and [2]
2- Set X = 0
3- Substitute X into Eqn. [1] to give Y
4-Substitute value of Y from step 3 into Eqn.[2] to give X
5- Go to Step ( 3 ).
To stop the iteration calculate [ Xi+1 – Xi ] for each
iteration and stop at appropriate value. [ very small value ].
This method is used for nonlinear equations.
258
The Bisection Method
Consider the equation:
Y= 20Xpower3- 59Xpower2- 33X+90
The smallest positive solution to this equation is required.
The dominant Xpower3 term, as X becomes large and +ve
Y becomes large and +ve and as X becomes large and –ve
Y becomes large and –ve. The behaviour of small values
of X can be determined by simple calculation.
259
Bisection Method
When X = 0 , Y = 90
When X = 1 , Y = 20 – 59 – 33 + 90 = + 18
When X = 2 , Y = 160 – 236 – 66 + 90 = - 52
This indicate there is a root between X> 1 & X< 2.
We can draw the function to a much greater accuracy the
section of the curve between
X=1 & X=2.
260
261
Bisection Method
We can use the fact : the value X=1[ for Y>0] is too small,
and the value X=2 [ for Y<0] is too large. By bisecting the
interval between the upper and lower bounds on the root
and testing the value of Y, the gap ( G ) between the upper
bound (U) and the lower bound (L) can be systematically
reduced. This is called the Bisection Method.
The algorithm for the method is given which constructed
to deal with the case where the curve has –ve gradient at
the point where it crosses the X-axis i.e.
F(L) >0 and F(U) < 0
262
Bisection Method
If the curve has +ve gradient at the root then the bounds
will be given by:
F(L) < 0 and F(U) > 0
In which case the first decision must be change to Y<= 0
263
Bisection Algorithm
1- Let L=1 and U= 2
2- G = U – L
3- X = L + G/2
4- Calculate Y = F ( X )
5- If Y >= 0 let L= X , go to 7
6- U = X
7- If G NOT sufficiently small Go to step 2
8- Print X
9- Stop
264
Numerical Integration
A) Trapezium Rule
265
Trapezium Rule
If the area under a curve f(x) is divided into n strips each
of width w, then the area is given by:
Area = w [ ½(Y0+Yn) + sum of remaining Y- sticks ]
Where Yi is the height above the axis of the point on the
curve whose x coordinate is xi.
PROOF: Divide the area into n parallel strips of width w.
Area under the curve = the sum of areas of all trapezium.
= w(1/2(Y0+Y1))+w( ½(Y1+Y2))+.........+w(1/2( Yn-1+
Yn))= w [ 1/2Y0+Y1+Y2+............+Yn-1+1/2Yn]
= w[ ½(Y0+Yn)+ sum of remaining Y sticks ]
266
Simpson's Rule
This rule states that if the area under a curve f(x) is divided
into 2n strips ( i.e. n pairs of sticks) each of width w, then
the area is given by:
Area= (w/3)[(Y0+Y2n) +( 4* sum of odd Y-sticks) + ( 2*
sum of even Y-sticks)]
PROOF:
For any three points on the curve ,it can be joined by a
parabola:
Y(x) = a *Xpower2 + b* X + c
267
Simpson’s Rule
268
Simpson’s Rule
The point P (-w. Y0) , Q ( 0, Y1) , R ( w, Y2)
The area under the curve is given by :
Area = integral f(x) dx from –w to w
= [ (aXpower3/3)+ (bX power2/2) +cX] from –w to w
={[(a*wpower3/3)+(b*wpower2/2)+c*w]- [(a*wpower3/3)+( b*wpower2/2) –c*w]}
= [ (2*a *wpower3/3) + 2* c* w ]
= (w/3 )* (2*a* wpower2 + 6* c )
269
Simpson’s Rule
Substituting the known values of x to find expressions for
the values of Y at the points P, Q, and R.
At P Y0= a* w power 2 – b* w + c
At Q Y1=
+c
At R Y2= a* w power 2 + b* w + c
By inspection
2*a * w power2 +6*c = Y0+4*Y1+Y2
Replacing the term in Area equation
Area = (w/3)*( Y0 + 4* Y1 + Y2)
270
Simpson’s Rule
271
Simpson’s Rule
Area= sum of the area beneath the curve f(x).
= Sum of areas of the n pairs of strips
= area of 1st pair+ area of 2sd pair +......+ area of nth
pair
= (w/3){ (Y0+4Y1+y2)+( Y2+4Y3+Y4)+..............+
(Y2n-2+4Y2n-1+Y2n)}.
Area= (w/3)*{( Y0+Y2n)+ ( 4* sum of odd Y sticks)+
( 2* sum of even Y- sticks )}
272
Ch. 5 Programming Languages
Historical perspective.
Traditional programming concepts.
Program units.
Language implementation.
Parallel computing.
Declarative programming.
273
Historical Perspective
Machine language - binary form direct
controls the hardware.
Assembly language - mnemonic form of
the machine language.
High-level programming language English like language.
Evolution?
274
Programming Lag.
The programming process required the programmer to
express all algorithms in the machine language.
First Generation
Second Generation
Machine Lang.
Assembly Lang.
15
5C
LD R5, Price
16
6D
LD R6, Shipping charge
50
56
ADDI R0,R5,R6
30 6E
ST R0, Total Coast
C0 00
HLT
275
Programming Lang.
Disadvantages:
1- Program written in assembly lang. Is
Machine Dependant
2- Programmer required to code instructions
in bit pattern form.
276
Historical Perspective
HLL
Machine
independent
Compiler
Assembler 1
Assembler n
Machine
dependent
Arch 1
Arch n
277
Programming Lang. [ HLL ]
The third generation
Their primitives were higher level and machine
independent.
FORTRAN [ FORmula TRANslation ]
COBOL
[ Common Business Oriented Language ]
A program called Translator, was written to translate
programs into machine language programs.
This translator often had to compile several machine
instructions into short sequences to simulate the activity
required by a single high level primitive. Thus these
translators were often called COMPILERS.
278
Historical Perspective
1st-generation - machine language.
2nd-generation - assembly language.
3rd-generation - machine independent.
4th-generation - software packages that
allow users to customize computer software
to their applications without needing
technical expertise.
5th-generation - declarative (logic)
programming.
279
Historical Perspective
Problems solved in an
environment in which
the human must conform
to the machine’s
characteristics
1st
Problems solved in an
environment in which
the machine conforms
to the human’s
characteristics
4th
280
Programming Lang.
Programming Languages are divided into
four groups:
1- Imperative Paradigm
2- Functional Paradigm
3- Object-Oriented Paradigm
4- Declarative Paradigm
281
IMPERATIVE PARADIGM
Imperative paradigm , also known as the
procedure paradigm [ Machine Languages,
FORTRAN, COBOL, ALGOL, BASIC,
APL, C , PASCAL , and ADA ]. It defines
the programming process to be the
development of a sequence of commands
that, when followed, manipulate data to
produce the desired result.
282
Functional Paradigm
It views the process of program
development as the construction of ( black
boxes) each accepts inputs and produces
outputs [ LISP, ML , Scheme ].
The primitives of a functional programming
lang. Consist of elementary functions from
which the programmer must construct the
more elaborate functions required to solve
the problem at hand.
283
Generations of programming
languages
284
Ex. Functional Paradigm
A function that
computes the
average of a list of
numbers
constructed from
the simpler
functions Sum,
Count, and Divide
285
Object- Oriented Paradigm
In this type units of data are viewed as
active “Objects’’ rather than the passive
units envisioned by the imperative
paradigm, [ SIMULA, Smalltalk, C++,
Ada95, Java ].
286
Example
Consider a list of names, in imperative paradigm the list is
considered merely a collection of data . Any program
accessing this list must contain the algorithms for
performing the required manipulations. Thus the list is
passive in the sense that it is maintained by a controlling
program rather than having the responsibility of
maintaining it self.
In the object-oriented approach, the list is constructed as an
object consisting of the list together with a collection of
procedures for manipulating the list.
287
Example
This may include procedures for:
Inserting a new entry in the list
Deleting an entry from the list
Detecting if the list is empty
Sorting the list
A program accessing the list does not need to contain
algorithms for performing these task.
288
Declarative Paradigm
It discover and implement a general
problem-solving algorithm, [ GPSS, Prolog]
The trick here is to discover and implement
a general problem-solving algorithm. Once
this is done , problems can be solved merely
by stating them in a form that is compatible
with this algorithm.
289
Traditional Programming
Concept
Statements in programming languages tend
to fall into three categories: declarative
statements, imperative statements, and
comments.
Declarative statements - define customized
terminology used in the program.
Imperative statements - describe steps in
the underlying algorithm.
Comments.
290
The composition of a typical
imperative program or program unit
291
Traditional Programming
Concept
Variables, constants, and literals.
Data type - integer, real, Boolean, char…..
Data structure - array, queue, list,……..
Assignment statements
Control statements.
Comments - internal documentation.
292
Variables and Data Types
High level language allows locations in
main memory to referenced by descriptive
names rather than by numeric addresses.
Such names is known as variables.
The type of data that will be stored at the
memory location associated with the
variable is known as data type.
Integer Real
Float
Boolean
293
The same variable declarations in
different languages
294
Data Structure
Data structure is the conceptual shape or
arrangement of data.
Ex. Text is viewed as long string of
characters
Sales Records : is viewed as a rectangular
table of numeric values.
295
Homogenous Array
A block of values of the same types.
Ex. One dimension array
Two dimension array
The individual component can be identified
by means of row and column and called
INDICES.
296
A two-dimensional array with two
rows and nine columns
297
Heterogeneous Array
It is block of data in which different
elements can have different types.
Ex. Block of data referring to an
employee may consist of:
1- An entry called Name
( Character )
2- An entry called Age
( Integer )
3- An entry called Skill Rating ( Real )
298
Declaration of heterogeneous arrays
in Pascal and C (continued)
299
Declaration of heterogeneous arrays
in Pascal and C
300
Constant and Literals
When a fixed predetermined value is used
in a program. Such value is called
LITERAL.
A = B+250 Where A and B are variables
and 250 is literal. Using literal in program is
not good programming it is better to use
CONSTANT.
Constant is descriptive name to be assigned
301
to specific, non changeable value.
Assignment Statements
Z=X+Y;
Z := X + Y ;
ZX+Y;
302
Control Statement
Control Statement is an imperative
statement that alter the execution sequence
of the program.
The simplest control statement is GO TO
EX. IF statement
Two options
WHILE statement
Two options
CASE statement
Many options
The loop structure
303
Control structures and their
representations in C, C++, C#, and
Java (continued)
304
Control structures and their
representations in C, C++, C#, and
Java
305
The for loop structure and its
representation in Pascal, C++,
C#, and Java (continued)
306
The loop
307
Comment
Programming languages provide ways of
inserting explanatory statements called
COMMENTS
Ex. /* This is a comment*/ C++,C, Java
// This is a comment .
C This is a comment.
FORTRAN
308
Program Units
Breaking large programs into manageable
units, units = modules, functions, objects.
Procedures and functions.
Parameter passing - formal parameters and
actual parameter, call by address and call by
value.
I/O statements.
309
Procedural Units
A procedure is a set of instructions for
performing a task that can be used as an
abstract tool by other program units.
Ex. Procedure Sort ( List )
The generic term (list) is called
PARAMETER
The terms used in writing the procedure is
called formal parameter.
310
Procedure
The precise meanings assigned to these
formal parameters when the procedure is
applied are called actual parameters.
When parameter is passed by value the data
in the calling program unit are never
changed. But if the parameters are passed
reference allows the procedure to modify
the data residing in the calling program.
311
The flow of control involving a
procedure
312
The procedure Project Population
written in the programming
313
Example
Procedure Demo ( Formal )
Formal = Formal + 1
314
Executing the procedure Demo and
passing parameters by value
(continued)
315
Executing the procedure Demo and
passing parameters by value
(continued)
316
Executing the procedure Demo and
passing parameters by value
317
Executing the procedure Demo and
passing parameters by reference
(continued)
318
Executing the procedure Demo and
passing parameters by
reference
(continued)
319
Executing the procedure Demo and
passing parameters by reference
320
Function
Function is a program unit similar to a
procedure except that a value is transferred
back to the calling program unit as the value
of the function.
321
The function CylinderVolume
written in the programming
language C
322
INPUT&OUTPUT Statement
EX. Read in ( value )
write in ( value )
323
An example of formatted output
324
Language Implementation
Translation - converting a program from
one language to another.
Translation involves three activities:
1. Lexical analysis,
2. Parsing, and
3. Code generation.
Lexical analysis - recognizing which
strings of symbols from the source program
represent a single entity.
325
The translation process
326
Lexical Analyzer
Ex. 153 should not interpreted as a 1 , a 5,
and a3. but should be recognized as single
numeric value [ 153 ] base 10.
Lexical analyzer generates a bit pattern
known as token to represent the unit and
hands the token to the parser. During this
process lexical analyzer skips overall
comment statements.
327
Language Implementation
Parsing - identifying the grammatical
structure of the program and recognizing
the role of each component.
Fixed-format languages{ FORTRAN} Vs.
free-format languages { C }.
Key words, reserved words, syntax
diagram, parse tree.
Coercion and strongly typed.
328
The Parser
The parser views the program in terms of
lexical units [ tokens ] rather than individual
symbols. The parser job to group these units
into statement.
Key words [ IF , THEN , ELSE ] are called
reserved words.
The parsing process is based on a set of
syntax rules.
329
The Parser
The syntax rules defined by syntax
diagrams.
Fixed Format Languages [ FORTRAN ]
Free Format Languages [ C
]
330
A syntax diagram of our if-then-else
pseudo code statement
331
Syntax diagrams describing the structure of a simple
algebraic expression
332
Parse tree for X+Y*Z
The parse
tree for the
string x + y
z
based on
the syntax
diagrams
333
Two distinct parse trees for the
statement if B1 then if B2 then S1
else S2 (continued)
334
Two distinct parse trees for the
statement if B1 then if B2 then S1
else S2 (continued)
335
Coercion
The statement X + Y
If X is integer and Y is real
The parser choose to have the code generator build the
instructions to convert one value to the other type and then
perform the addition such implicit conversion between
types is called Coercion.
Strongly typed: means that all activities requested by a
program must involve data of agreeable types without
coercion.
Parsers for these lang. Report all type conflicts as Errors
336
Language Implementation
Code generation - constructing the machine
language instructions to simulate the
statements recognized by the parser.
Code optimization.
Linker - links all necessary object programs
to produce a complete, executable program.
Loader - place the program in memory for
execution (what about multitasking?)
337
If the object program is requests services from
other programs. The task of making these
connections is preformed by a program called
LINKER .
Its job to link several object programs. The result
is executable program called LOAD module.
The module is placed in Memory by a program
called a LOADER which is a part of Operating
System.
338
An object-oriented approach to the
translation process
339
The complete program preparation
process
340
Parallel Computing
Developing languages for describing
processes that execute simultaneously.
Ada.
Linda - tuple space (a shared storage area),
in which each process in the system can
deposit and retrieve data bundles.
341
Object-Oriented Programming
The object oriented programming
entails the development of active
program units called objects , each
of which contains procedures
describing how that object should
respond to various stimuli.
342
Object-Oriented
The object oriented approach to a problem
is to identify the objects involved and
describe them as self-contained units.
343
Classes and Objects
Consider the task of developing a computer game in which
the player must protect the Earth from falling meteors by
shooting them with high power lasers. Each laser contains
a finite internal power source that is partially consumed
each time the laser is fired. Once this source is depleted the
laser becomes useless. Each laser should be able to
respond to the commands to aim further to the right, aim
further to the left, and to fire it’s laser beam.
344
Classes and Objects
In object oriented, each laser in the game would be
implemented as an object that contains a record of its
remaining power as well as procedures for modifying its
aim and firing its laser beam. Since all the laser objects
have the same properties, they can be build from the same
template. This template is called class.
A variable that resides within an object, such as Remaining
Power, is called an INSTANCE variable and the
procedures within an object are called Methods
345
The structure of a class describing a
laser weapon in a computer game
346
CONSTRUCTORS
In the game we might want the different
laser to have different initial power setting.
This is done by special methods called
CONSTRUCTORS. Initialization needs are
handled by defining special methods called
constructors
347
A class with a constructor
348
ENCAPSULATION
It refers to restricting access to an objects
internal properties. To say that certain
features of an object are encapsulated
means that only the object itself is able to
access them.
349
Our Laser-Class definition using encapsulation as it
would appear in a Java or C# program
350
DECLARATIVE
PROGRAMMING
Logical Deduction
Suppose we know that either
Ali is on stage
Or Ali is sick
And we are told that: Ali is not on stage,
We could the conclude that Ali must be sick
This is an example of a deductive-reasoning
principle called RESOLUTION
351
Declarative Programming
Example:
Let A: Ali is a prince.
B: Miss Salma is an actress.
Then A OR B
Means
Ali is a prince or Miss Salma is an actress.
And B AND ( NOT A )
Miss Salma is an actress and Ali is not a
prince
352
Example
And A  B ( A implies B )
Ali is a prince implies Miss Salma is an
actress
In general if :
P OR Q and R OR ( NOT Q )
We can conclude the statement P OR R
353
Example
The two original statements resolve to form
the third statement which it is called
RESOLVENT.
Prolog Programming language is example
of declarative programming
Prolog is Programming in logic
354
Declarative Programming
Resolution can be applied only to pairs of
statements that appear in clause form-that is
statements whose elementary components
are connected by the Boolean operation OR.
Thus P OR Q is In clause form whereas
P -> Q is not.
355
Resolving the statements (P OR Q) and
(R OR  Q) to produce (P OR R)
356
Declarative Programming
Inconsistent- in a collection of statements, it
is impossible for all the statements to be
true at the same time.
A simple example a statement of the form P
combined with the statement NOT P.
The rule is that if repeated application of the
resolution produces the empty clause [ the
result of resolving a clause of the form with
357
a clause of the form NOT P ]
Declarative Programming
Then the original collection of statements
must be inconsistent.
358
Resolving the statements (P OR Q),
(R OR Q), R, and P
359
Ch. 6 Software Engineering
The software engineering discipline.
The software life cycle.
Modularity.
Development tools and techniques.
Documentation.
Software ownership and liability.
360
The Software Engineering
Discipline
How to develop and manage a large
program (>100K lines of code) or a huge
program (>1M lines of code)???
What is software engineering discipline?
What is the quantitative system (metrics) to
measure the quality and successfulness of
the underlying software development???
Developing techniques for immediate
applications and for future applications.
361
The Software Life Cycle
Development
Use
Modification
362
The Software Life Cycle
Analysis
Design
Development phase
Implementation
Testing
363
The Software Life Cycle
Waterfall model.
Computer-aided software engineering
(CASE).
Prototyping.
364
A structure chart for a simple
Internet “mail order” business
365
A class diagram for a simple Internet
“mail order” business
366
A structure chart showing data
coupling
367
A collaboration diagram of a simple
Internet “mail order” business
368
Logical and functional cohesion
within an object representing an
order form in a simple Internet “mail
order” business
369
Modularity
Modular implementation - structure chart.
Coupling - control and data coupling.
Implicit coupling, global data - why is not
good? Side effects!
Cohesion - the coupling between modules.
Logical cohesion and functional cohesion.
370
The publisher-subscriber pattern
371
The component-container pattern
372
Development Tools and
Techniques
Top-down design.
Bottom-up design.
Dataflow diagrams - a pictorial
representation of data paths.
Entity-relationship diagrams - a pictorial
representation of the items of information
(entities) within the system and the
relationships between these pieces of
information.
373
A dataflow diagram of a simple
Internet “mail order” business
374
An entity-relationship diagram
375
One-to-one, one-to-many, and
many-to-many relationships between
entities of types X and Y
376
Development Tools and
Techniques
Data dictionaries - a central depository of
information about the data items appearing
throughout the system.
Enhancing communication between the
potential user of the system.
Establishing uniformity throughout the
system.
377
Documentation, software
Ownership and Liability
User documentation and system
documentation.
Copyright and patent laws.
378
Part III: Data Organization
Data structures.
File structures.
Database structures.
379
DATA STRUCTURES
Data structure is concerned with the various
ways that data files can be organized and
assembled.
The structures of data files will strongly
influence the selection of a computational
strategy for a given data processing job.
380
DEFINITIONS
1- A List is a collection of sets of data
elements. LIST and FILE have the same
meaning.
2- Nodes : The sets of data contained in a
list are referred to as Node. Each node will
contain several related data items. A node is
equivalent to a record.
381
DEFINITION
The items within a node are required to be
stored in storage locations, though the nodes
themselves need not be adjacent to one
another.
3- Field: Each of the items within a given
node is said to occupy a field. A field can
contain either a data item or a link [or
pointer].
382
DEFINITION
4- The Link [ Pointer]: Contain the starting
addresses of other nodes, thus creating a
structural interrelationship among the
nodes.
383
Novels arranged by title but linked
according to authorship
384
DEFINITION
The link tell us that the next node in the list
has starting address of xxxx.
A node need not be restricted to one link,
multilinked nodes are common. Each of the
nodes in a given list should contain the
same number of links.
385
DEFINITION
Linear Lists:
A linear list is a list which the nodes are ordered in
one dimensional arrangement. [ N1, N2, ---, Nr]
Sequential linear list
When Nodes are stored consecutive adjacent to one
another. We refer to the list as sequential linear list
.[M/C language, Computer programs, Magnetic
tape data files are usually stored in the form of
sequential linear list].
386
LINKED LINEAR LIST
Most searching, sorting, and merging
operations can be carried out quite easily
with sequential linear lists. The simple type
of the linked list is the single linked linear
list. Each node will contain one pointer,
which indicates the starting address of the
next node in the list.
387
The array of Readings stored in
memory starting at address
388
A two-dimensional array with four
rows and five columns stored in row
major order
389
Names stored in memory as a
contiguous list
390
The structure of a linked list
391
Deleting an entry from a linked list
392
Inserting an entry into a linked list
393
A procedure for printing a linked list
394
MULTIPLE LINKS
If each node in the list contains several
items, each of which should be ordered in
some particular manner, then a different set
of links is established for each item.
Each set of links establishes a separate
single linked linear list.
395
STACKS
A stack is a list in which all insertions and
deletions are performed at the same end of
the structure.
Stack known as last-in, first-out [LIFO]
structures.
396
BACKTRACKING
A classic application of a stack occurs when a
program unit requests the execution of a
procedure. If the procedure itself request the
execution of another procedure, and so on .
Stack is an ideal structure for such a system.
This process is called Backtracking
397
Nested procedures terminating in the opposite
order to that in which they were requested
398
BACKTRACKING
Suppose we want to print the names in a
linked list in a reverse order that is last
name first.
We can access the names only by following
the link structure. Thus we need to use the
stack.
399
Using a
stack to
print a
linked
list in
reverse
order
(continue
d)
400
Using a
stack to
print a
linked list
in reverse
order
401
A procedure (using an auxiliary stack)
for printing a linked list in reverse order
402
A stack in memory
403
Stacks
Last-in first-out.
Push and pop.
Using stacks for maintaining procedure
calls.
Other applications???
404
QUEUES
Queue is a list in which all insertions are
performed at one end while all deletions are
made at the other end.
The end at which entries are removed is
called the Head [ or sometimes the Front] of
the queue. The end of the queue at which
new entries are added is called the Tail[ or
Rear].
405
QUEUES
QUEUE uses two memory cells to use as
pointers instead of just one .
Head Pointer
Tail Pointer
406
A queue implemented with head and tail
pointers
407
A queue “crawling” through memory
408
A circular
queue
(a)
containing
the letters F
through O
as actually
stored in
memory
409
A circular queue (b) in its conceptual form in
which the last cell in the block is “adjacent”
to the first cell
410
Queues
First-in first-out.
Head and tail.
Circular queue.
411
TREES
The last Data structure that we will consider
is the tree, which is the structure reflected
by an organization chart of a typical
company.
412
An example of an organization
chart
413
TREES
Tree consists of a collection of nodes which
are interconnected by branches to form a
tree like configuration, as shown below:
414
Tree terminology
415
TREES
The branches emanate downward from the
nodes, The node is presented by a square.
The top node is called the root of the tree.
All of the remaining nodes are called either
branch or terminal nodes.
The branch nodes have branches emanating
from them, the terminal nodes do not.
416
TREES
The degree of a node is equal to the number
of sub-trees that emanate from that node.
Terminal nodes are therefore nodes of
degree zero.
All trees have two or more levels. The root
is the 1st level. The subsequent nodes
increase in level as they branch out from the
root.
417
BINARY TREE
It is tree in which each node has at most two
children. This trees are stored in memory
using a linked structure with two pointer
called:
Left Child Pointer
Right Child Pointer
418
The structure of a node in a
binary tree
419
The conceptual and actual organization of a
binary tree using a linked storage system
420
A tree stored without pointers
421
BINARY TREE
Another alternative storage system
The location of a node’s parent can be
found by dividing the node’s position in the
block by 2 while discarding any remainder
[the parent of the node in position 7 would
be the node in position 3]
422
BINARY TREES
The location of a nodes sibling can be found
by adding 1 to the location of a node in
even position or subtract 1 from location of
a node in an odd position.
Node 4 ( 4+1)= 5
Node 3 ( 3- 1 )= 2
423
A sparse, unbalanced tree shown in its
conceptual form and as it would be stored
without pointers
424
Searching the list
If the list were stored according to the
linked list model , we would be forced to
search the list in sequential fashion, a
process could be very inefficient if the list
should become long. We will seek an
implementation that allows us to use the
binary search algorithm.
425
Searching The List
To apply this algorithm , our storage system
must allows us to find the middle entry of
successively smaller portion of the list.
This can be done using linked binary tree.
Ex. The list of letters [ A, B, C, D, E, F, G,
H, I, J, K, L, and M ]
426
The letters A through M arranged in an
ordered tree
427
The binary search as it would appear if
the list were implemented as a linked
binary tree
428
The successively smaller trees considered by
the procedure in when searching for the letter J
429
Printing a search tree in alphabetical
order
430
A procedure for printing the data in a
binary tree
431
Inserting the entry M into the list
B, E, G, H, J, K, N, P stored as a tree
(continued)
432
Inserting the entry M into the list
B, E, G, H, J, K, N, P stored as a tree
433
A procedure for inserting a new entry in
a list stored as a binary tree
434
Trees
Trees - an organization chart; e.g., family
tree and company’s organization .
Root node, leaf nodes, arc, sub trees.
Parent, children, siblings.
Depth of a tree.
Tree implementation.
Binary tree.
435
A stack of integers implemented
in C++
436
A stack of integers implemented in
Java and C#
437
Our first attempt at expanding the machine
language to take advantage of pointers
438
Loading a register from a memory cell that is
located by means of a pointer stored in a
register
439
Customized Data Types
User-defined types - allow programmers to
define additional data types using the
primitive types and structures as building
blocks.
Abstract data types - encompasses both the
storage system and the associated
operations.
Encapsulation.
440
The role of an operating system when
accessing a file
441
Object-Oriented Programming
Objects.
Methods (or member functions).
Class.
Inheritance.
442
Ch. 8 File Structures
Sequential files.
Text files.
Indexed files.
Hashed files.
The role of the operating system.
443
Maintaining a file’s order by
means of a file allocation table
444
Sequential Files
When to use it? When all the records need
to be proceeded, it makes no difference
which records are proceeded first.
If the storage device is a tape system, we
normally follow the sequential order
because of the sequential nature of the tape
itself. What’s about a disk system???
EOF and sentinel.
How to update a sequential file?
445
Sequential Files
In PASCAL, statements read() and write()
are used to retrieve and deposit information.
Transaction file
Old master file
Merge Alg. See Figure8.3
New master file
446
Text Files
Text file - the size of the logical records in
a sequential file to a single byte (Char).
How to manipulate a text file? A word
processor?
How to use text files to define an input and
an output files to a program?
447
Indexed Files
If you need to retrieve records in the file in
an arbitrary order throughout the day, what
is the main problem when you use a
sequential file to store the records?
What’s the fast way to find the subject you
are interesting in from a book??? Ans.
Using the index.
448
Indexed Files
An index for a file consists of a listing of
the key field values occurring in the file
along with the location in mass storage of
the corresponding record.
Key field.
An inverted file - primary key and
secondary key.
When records are inserted and deleted, all
indexes must be updated.
449
The structure of a simple employee file
implemented as a text file
450
The first two bars of Beethoven’s Fifth
Symphony
451
Converting data from two’s complement
notation into ASCII for storage in a text file
(continued)
452
Converting data from two’s complement
notation into ASCII for storage in a text file
453
Indexed Files
Index size - since the index must be moved
to main memory to be searched, it must
remain small enough to fit within a
reasonable memory area.
What if the index size is too large???
The partial-index structure.
An index to the index.
454
Hashed Files
Sequential files - process in a serial order.
Indexed files - direct access (random
access) . Overhead: maintaining an index
table.
Hashed files - reduce the overhead by
computing the location of a record in mass
storage by applying an algorithm to the
value of the key field in question.
455
Opening an indexed file
456
An inverted file
457
Hashed Files
A particular hashing technique:
1. Divide the mass storage area allotted to
the file into several sections called buckets.
2. Convert any key field value into a
numeric value.
3. Divide any key field value stored in
memory by the number of buckets.
4. Convert any key field value into an
integer that identifies the bucket in memory.
458
A file with a partial index
459
Hashed Files
What is the main concern when using
hashed files?
Distribution problems - once we have
chosen the hash algorithm, we have no
control over the distribution of records in
mass storage.
Clustering problem - majority of records
are placed in the same bucket and the rest of
buckets contain almost no records.
460
The rudiments of a hashing system, in which
each bucket holds those records that hash to
that bucket number (continued)
461
The rudiments of a hashing system, in which each
bucket holds those records that hash to that bucket
number
462
Hashed Files
Overflow problem - unless the buckets are
extremely large, overflow may occur.
Goal - how to select a hash algorithm that
evenly distributes the records among the
buckets.
Division method.
The midsquare method.
The extraction method.
463
Hashing the key field value 25X3Z
to one of 40 buckets
464
Handling bucket overflow
465
Hashed Files
Collision - more than one record will hash
to the same bucket.
Assume insert records into 41 buckets:
the probability of placing the 1st record to
an empty bucket is 41/41, the 2nd is 40/41,
the 3rd is 39/41 and so on. The probability
of placing 8 records into 8 empty buckets is
(41/41)(40/41)(39/41)….(34/41) = .482
Less than 50%!!!
466
A large file partitioned into buckets
to be accessed by hashing
467
Hashed Files
The high probability of collisions indicates
that a hashed file should never be
implemented under the assumption that
clustering will never occur.
How to handle the overflow problem?
Reserve an additional area of mass storage
to hold overflow records.
Double hashing method.
468
The Role of the Operating
System
Operating systems need to manipulate files
to perform designated tasks.
Operating systems maintains a table called
a file descriptor or file control block for
each file being processed.
In PASCAL, file descriptors can be created
by assign() and reset().
469
Ch. 9 Database Structures
General issues.
The layered approach to database
implementation.
The relational model.
Object-oriented databases.
Maintaining database integrity.
470
General Issues
A file Vs. a database organization.
Why needs a database system?
The consolidation approach - advantage:
central control, disadvantage: security.
Database administrator (DBA).
Access privileges - schema and subschema.
Other issues - size and scope, privacy.
471
The Layered Approach to
Database Implementation
End user
Application software
Data seen in terms of
the applications
Data seen in terms of
a database model
Database management
system
Actual database
Data seen in its actual
organization
472
The Layered Approach to
Database Implementation
Database management system (DBMS).
The advantages of the separation of
application software and the database
management system:
1. Simplify the design process - for example
the distributed database.
2. Providing a central controlling access to
the database.
473
A file versus a database organization
(continued)
474
A file versus a database organization
475
The Layered Approach to
Database Implementation
3. Data independence - the ability to change
the organization of the database itself
without changing the application software.
4. Allows the application software to be
written based on a simplified, conceptual
view of the database (database model)
instead of the actual complex database
structure.
Host languages.
476
The Relational Model
Relation - tuple (row) and attribute
(column).
How to make up the database using the
relations of data?
Extending the relation - pro and con?
Dividing information into various relations
(nonloss decomposition) - pro and con?
477
The Relational Model
Relational operations:
The SELECT operation.
The PROJECT operation.
The JOIN operation.
The SQL (Structured Query Language).
478
Object-Oriented Databases
Why object-oriented databases:
1. Data independence can be achieved by
encapsulation.
2. The concepts of classes and inheritance
fit schemas and subschemas of databases.
3. Intelligent data objects that can answer
questions themselves.
4. It may overcome some of the restrictions
inherent in other database models.
479
Maintaining Database Integrity
Why database integrity is important?
The commit/rollback protocol.
Cascading roll back.
Locking protocol - shared locks and
exclusive locks.
Wound-wait protocol.
480
A relation containing employee
information
481
A relation containing redundancy
482
An employee database consisting of
three relations (continued)
483
An employee database consisting of
three relations (continued)
484
An employee database consisting of
three relations
485
Finding the departments in which employee
23Y34 has worked (continued)
486
Finding the departments in which employee
23Y34 has worked
487
A relation and a proposed decomposition
488
The SELECT operation
489
The PROJECT operation
490
The JOIN operation
491
Another example of the JOIN operation
492
An application of the JOIN operation
(continued)
493
An application of the JOIN operation
494
PART IV: The Potential of
Algorithmic Machines
Artificial Intelligence.
Theory of Computation.
495
The associations between objects in an
object-oriented database
496
Ch. 10 Artificial Intelligence
Some philosophical issues.
Image analysis.
Reasoning.
Control system activities.
Using Heuristics.
Artificial neural networks.
Applications of AI.
497
Some Philosophical Issues
Machines Vs. humans.
Performance Vs. simulation.
Intelligence as an interior characteristic Turing test and program DOCTOR
(ELIZA).
How to create an intelligent machine?
498
An Intelligent puzzle-solving
machine
This machine takes the form of a metal box
equipped with a gripper, a video camera,
and a finger with a rubber end so that it
does not slip when pushing something.
Actions:
1. Turn on the machine.
2. Place the puzzle.
3. The finger pushes the tiles back to the
original order.
4. Turn off the machine.
499
Image Analysis
The first intelligent behavior required by
the puzzle-solving machine is the extraction
of information through a visual medium.
Perceive ability - determine the current
status of the puzzle.
Optical character readers.
Character recognition based on matching
the geometric characteristics.
500
Reasoning
Is possible to develop proper programs
targeted to all possible initial configurations
(in total 181,440 of them)?
Develop a program which can solve the
problem itself - the ability to make
decisions, draw conclusions, and in short,
perform elementary reasoning activities.
501
Our puzzle-solving machine
502
The eight-puzzle in its solved
configuration
503
Reasoning
A production system consists of three
main components:
1. A collection of states - start/goal states.
2. A collection of productions (rules).
3. A control system - which consists of the
logic that solves the problem of moving
from the start state to the goal state.
State graph - conceptualizing all states,
rules, and preconditions in a production
504
system.
Reasoning
Socrates is a man.
All men are humans.
All humans are mortal.
Start state
Goal state
Socrates is a man.
All men are humans.
All humans are mortal.
Socrates is a human.
Socrates is a man.
All men are humans.
All humans are mortal.
Socrates is a human.
Socrates is mortal.
505
Control System Activities
A state-graph traversal problem.
Search tree.
How to build a search tree?
It is impractical to develop a full search
tree for a complex problem.
Using depth-first construction instead of
breadth-first manner.
Avoiding redundancy.
506
Using Heuristics
Heuristics - the use of intuition, a rule of
thumb which may lead to a correct direction
but offer no assurance on it.
How to develop a heuristic - first develop a
quantitative measure by which a program
can determine which of several states is
considered closest to the goal (cost
function).
507
Artificial Neural Networks
Neural networks - model networks of
neurons in living biological systems.
Compute effective
inputs
Threshold
value
Output
0 or 1
I1W1+…+InWn
508
Applications of Artificial
Intelligence
Language processing.
Robotics.
Database systems.
Expert systems.
509
A small portion of the eight-puzzle’s
state graph
510
Deductive reasoning in the context of a
production system
511
An unsolved eight-puzzle
512
A sample search tree (continued)
513
A sample search
tree (continued)
514
A sample
search
tree
(continue
d)
515
A sample
search tree
516
Productions stacked for later
execution
517
An unsolved eight-puzzle
518
An algorithm for a control system
using heuristics
519
The beginning of our heuristic
search
520
The search tree after two passes
521
The
search
tree
after
three
passes
522
The
complete
search
tree
formed
by our
heuristic
system
523
A neuron in a living biological system
524
Ch. 11 Theory of Computation
A bare bones programming.
Turing machines.
Computable functions.
A noncomputable function.
Complexity and its measure.
Problem classification.
525
The activities within a processing unit
526
Representation of a processing unit
527
A neural network with two different
programs (continued)
528
A Bare Bones Programming
Language
A universal programming language - a
language encompasses the power of
algorithmic processes themselves; i.e., if a
problem can be solved algorithmically, the
an algorithm for solving the problem can be
expressed in the language. On the other
hand, if the problem can not be expressed in
the language, there is no such an algorithm
to solve the problem.
529
Uppercase C and uppercase T
530
Various orientations of the letters
C and T (continued)
531
The structure of the character
recognition system
532
The letter C in the field of view
533
The letter T in the field of view
534
An artificial neural network implementing an
associative memory
535
The steps leading to a stable
configuration (continued)
536
The steps leading to a stable
configuration
537
Crossing two poker-playing
strategies
538
Coding the topology of an artificial
neural network (continued)
539
Coding the topology of an
artificial neural network
540
A semantic net
541
A Bare Bones Programming
Language
Data description statements - all variables
are considered to be of type “bit pattern of
any length.” => no need a declarative part.
Process description statements - three
assignment statements: clear, incr, decr and
one control structure: while-end.
542
An attempt to display the function that
converts measurements in yards into meters
543
The components of a Turing machine
544
A Bare Bones Programming
Language
“move tax to extra”
Clear aux;
clear extra;
while tax not 0 do;
incr aux;
decr tax;
end;
while aux not 0 do;
incr tax;
incr extra;
decr aux;
end;
545
Turing Machines
Turing machines - are conceptual devices
for studying the power of algorithmic
processes.
A Turing machine consists of a control unit
that can read and write symbols on a tape
The machine must be in one of a finite
number of states, start/halt states.
546
A Turing machine for incrementing a value
547
A Bare Bones program for computing
XY
548
A Bare Bones implementation of the
instruction “copy Today to Tomorrow”
549
Turing Machines
Today’s computers <=> Turing machines
finite memories <=> infinite supply of tape
CPU
<=> the control unit
bit patterns
<=> states
The significance of Turing machines in
theoretical computer science - the
computation power of Turing machines is
as great as any algorithmic system.
550
Computable Functions
How to measure computing power?
Goal: using Turing machines to investigate
the power of the bare bones language.
Computing the functions is the process of
determining an output of a function from its
inputs.
If one machine is capable of computing
more functions than another, the former is
considered the more powerful.
551
Computable Functions
Ex. A system in which function outputs are
predetermined and recorded in a table.
Ex. Finding function outputs would be to
describe how to compute the output.
Computable - the functions whose output
values can be determined algorithmically
from their input values.
Noncomputable functions!
552
Computable Functions
Turing computable.
The Church-Turing thesis.
If a computational system is capable of
computing all the Turing-computable
functions, it is considered to be a universal
system.
Apply the Church-Turing these to confirm
that the bare bones language is a universal
programming language.
553
A Noncomputable Function
Computing the Godel number.
The halting problem.
554
Complexity and Its Measure
Time and storage complexities (Big O).
Order of complexity.
Polynomial and nonpolynomial problems.
NP problems - nondeterministic
polynomial problems.
NP-complete problems.
555
Testing a program for self-termination
556
Proving the unsolvability of the halting
program (continued)
557
Proving the unsolvability of the halting
program (continued)
558
Proving the unsolvability of the
halting program
559
Roadmap to Computer Science
Study
Fundamental courses: Physics,
Mathematics, and Introduction to Computer
Science.
Software:
1. Fundamental: Problem Solving and
Programming, Data Structure, Algorithm,
and Software Engineering.
2. Language: Assembly Language,
Programming Language, C, and JAVA.
560
A procedure MergeLists for
merging two lists
561
The merge sort algorithm implemented as a
procedure MergeSort
562
The hierarchy of problems generated by the
merge sort algorithm
563
Graphs of the mathematical expressions n, lg
n, n lg n, and n2
564
A graphic summation of problem
classification
565
Encrypting a bit pattern as a
knapsack problem
566
Public key encryption using
knapsack problems
567
Constructing
a public key
encryption
system
568
Roadmap to Computer Science
Study
3. Theory: Formal Language and Theory of
Computation.
4. System: Operating System, Compiler,
Networking, Database, and Multimedia.
Hardware:
1. Fundamental: Electronics, Logic Design,
Digital System Design, and Computer
Architecture.
569
Roadmap to Computer Science
Study
2. System: Microprocessors and VLSI
design.
Applications:
1. Consumer products.
2. Artificial Intelligence.
3. Networking.
4. Image Processing.
5. Computer Architecture and Compiler.
570
Roadmap to Computer Science
Study
6. VLSI and Computer-Aided Design.
7. Biological (Medical) Computing.
8. Multimedia.
9. Databases.
10. Education.
11. Business and management.
12. And more!!!
571