Transcript Relational Database Management Systems

Knowledge Discovery in
Databases
&
Information Retrieval
University of Texas at Austin
School of nformation
i
Knowledge Management Systems
Presented April 29, 2003
By Anne Marie Donovan

Knowledge Discovery in Databases


“The nontrivial process of identifying valid,
novel, potentially useful, and ultimately
understandable patterns in data” (Fayyad,
Piatetsky-Shapiro, and Smyth, 1996, p. 30)
Also known as knowledge extraction,
information harvesting, data archeology,
and information extraction (p. 28)

Information Retrieval
“The methods and processes for searching
relevant information out of information
systems that contain extremely large
numbers of documents” (Rocha, 2001, 1.1)
 “The ultimate goal of IR is to produce or
recommend relevant information to users”
(1.2)
 “Traditional IR does not identify users and
classifies subjects only with unchanging
keywords and categories” (1.2)


Institutions that use KDD/IR systems
Require knowledge-based decisions
 Have a large quantity of accessible, relevant,
historical and current data
 Have a high payoff for correct decisions
 Financial: banking & investment
 Medical: healthcare & insurance
 Sales: marketing & customer relations
(Piatetsky-Shapiro, 1998, Slides 28-31)


Database Management Systems

File Systems

Relational Database Management Systems
(RDBMS)

Object-Oriented Database Management
Systems (OODBMS)

Object-Relational Database Management
Systems (ORDBMS)
(Devarakonda, 2001, ORDBMS)

Relational Database Management
Systems (RDBMS)
Relational databases are composed of many
relations in the form of two-dimensional
tables of rows and columns
 RDBMS advantages include the SQL
standard (enables migration between
database systems), rapid data access and
large storage capacity
 RDBMS disadvantages include an inability
to handle complex data types and
relationships
(Devarakonda, 2001, RDBMS)


Object-Oriented Database Management
Systems (OODBMS)
OODBMS use abstract data types (ADTs) in
which the internal data structure is hidden
 OODBMS data is managed through two sets
of relations, one describing the interrelations
of data items and another describing the
abstract relationships
 OODBMS handle complex data
relationships, but suffer from poor
performance and problems of scalability
(Devarakonda, 2001, OODBMS)


Object-Relational Database Management
Systems (ORDBMS)
ORDBMS store all database information in
tables, but some entries have richer data
structure that are also called abstract data
types (ADTs).
 ORDBMS exhibit features of both the
relational and object models such as
scalability and support for rich data types
 Their main advantage is massive scalability
(Devarakonda, 2001, ORDBMS)


The KDD Process
Collecting and pre-processing data
 The problem of continually increasing
volumes of data
 The problem of increasingly complex
forms of data
 Identifying and extracting useful knowledge
from large data repositories
 What knowledge is in the data set?
 What can be observed about the data set?
 Presenting the knowledge in usable forms
(Fayyad et al., 1996)


The KDD Process (continued)
Data management problems in data
collection, storage, and retrieval
 Translation, change detection, integration,
duplication, summarization; aggregation,
timeliness/datedness (Widom, 1995)
 The impracticality of manual analysis
 Billions of records and hundreds of fields
 Increasing desire for on-the-fly analysis
and more flexible presentation (Fayyad et
al., p. 28)


The KDD Process (continued)
A need to automate the knowledge discovery
and extraction processes
 Data selection and pre-processing
 Data transformation and mining
 Interpretation and evaluation (p. 28)
 Automation requires attention to:
 Data collection, storage, and retrieval
 Statistical foundations of search and
retrieval processes (p. 29)


Stages in the KDD process
Learning the application domain
 Creating a target data set
 Data cleaning and preprocessing
 Data reduction and projection
 Choosing the function of data mining
 Choosing the data mining algorithm
 Data mining
 Interpretation
 Using discovered knowledge (pp. 30-31)


Data mining
The application of specific algorithms to a
data set for the purpose of extracting data
patterns (p. 28)
 “Fitting models to or determining
patterns from observed data” (p. 31)


Data warehousing

Collecting and “cleaning” transactional
data to make it available for online
analysis and decision support (p. 30)

Data mining tasks
Classification: predicting an item class
 Forecasting: predicting a parameter value
 Clustering: finding groups of items
 Description: describing a group
 Deviation detection: finding changes
 Link analysis: finding relationships and
associations
 Visualization: presenting data visually to
facilitate human discovery (Piatetsky-Shapiro,
1998, Slide 17)


Components of data mining systems
Model functions: classification, regression,
clustering, etc. (pp. 31 -32)
 Model representation: decision trees and
rules, linear models, non-linear models,
example-based methods, etc. (p. 32)
 Preference criterion: quantitative criterion
embedded in the search algorithm; implicit
criterion embedded in the KDD process
 Search algorithms: parameter search (given
a model) or model search over model space


There is NO universal search algorithm
Each type of search suits specific types of
search problems
 The searcher must be careful to properly
formulate the question
 The searcher must understand the search
goal (p. 31)


Every search can be improved by an
increase in data or query context

Creating context for KDD and IR
Extending IR throughout the social network
of an organization, e.g., Answer Garden
(Ackerman, 1994 & Ackerman and
MacDonald, 1996)
 Providing social context for data exchange,
e.g., PeopleGarden (Xiong and Donath, 1999)
 Relational database reverse engineering,
“extracts a conceptual model from an
existing relational database by analyzing
data instances as well as metadata” (Lee and
Hwang, 2002, Conclusion)


KD & IR problems for Web resources
Collecting and pre-processing data
 Even more continually changing data
 Complex data; streaming & multi-media
 The problem of identifying and extracting
useful knowledge from Web resources
 No consistent data models; no context
 A lack of descriptive information
 Presenting the knowledge in usable forms
 More and more wireless devices and timesensitive, multi-media applications


Current methods for Web KD & IR
Collecting and pre-processing data
 Web crawlers and link-based ranking
 Human indexing and categorization
 Identifying and extracting useful knowledge
from Web resources
 Keyword search on natural language text
 Topical directories or topical Web sites
 Presenting the knowledge in usable forms
 Content presented in native format
(plugins) or in HTML


Automating KD & IR for the Web
Semantic markup to enable machine
understanding/processing (RDF/S &
DAML/OIL) & inference analysis
 Intelligent search engines and agents to
exploit semantic statements
 Ontologies to provide context (a data
model) for agents (Shah et. al.)


Automating KD & IR for the Web
(continued)
Automated data collection, automated
context collection (data pre-processing)
 Value-added services (query routing)
 Integrated query systems/knowledge
delivery systems (accessibility)
 Social accounting metrics to provide
context for humans (Smith, 2002, p. 52)


Enhanced presentation for the Web

Reformatting for presentation
 Differentiated service
 Variable visualization
• Adaptive graphics, “a unifying
framework that allows visual
representations of information to be
customized and mixed together into
new ones” (Boier-Martin, 2003, pp. 6-9)
• Previewing & interactive content
• Selective presentation & customized
views

KDD and IR for pervasive computing

Achieving “ubiquitous data access”
(Cherniack, Franklin, & Zdonik, 2001, slide 7)
 Data management problems
• Dissemination (context dependent
pull/push)
• Synchronization (multiple
collectors/devices)
• Recharging (renewing) multiple data
streams
• Profile-driven data management

KDD and IR for pervasive computing
(continued)

Achieving “ubiquitous data access”
(Cherniack, Franklin, & Zdonik, 2001, slide 7)
 Location aware, mobile devices
 Service discovery for mobile services
 Distributed sensors/collectors (slides 827)

Next generation KDD & IR will….
Focus on solving business problems, not data
analysis problems
 Embed knowledge discovery engines
 Integrate access to enterprise and external
data on the back-end
 Integrate knowledge discovery process with
knowledge delivery tools (Piatetsky-Shapiro,
1998, Slide 7)


Next generation KDD & IR will….
Manage information retrieval contextually
 Allow contextual query/continuous query
 Synchronize multiple data flows from
disparate sensors/input devices
 Enable KD in virtual networks of peer-topeer databases (data “clusters” or “cubes”)
 Interpolate or extrapolate for missing data
(Cherniack et. al., 2001, slides 115-138)


Next generation KDD & IR will….
Recognize individual users
 Characterize information resources
 Provide a way to exchange knowledge
between users and information resources
(push and pull of information
 Adapt to the user community and enable the
reuse and recombination of information as
well as its exchange
(Rocha, 2001, 1.2)


KDD research problems
Massive data sets & high dimensionality
 User interaction & prior knowledge
 Determining statistical significance
 Missing data
 Understandability of patterns
 Management of changing data & knowledge
 Data integration
 Non-standard, multimedia, & objectoriented data (Fayyad, Piatetsky-Shapiro, &
Smyth, 1996, pp. 33-34)


“Top Ten” IR research issues
Integrated solutions
 Distributed IR
 Efficient, flexible indexing and retrieval
 "Magic” (automatic query expansion)
 Interfaces and browsing
 Routing and filtering
 Effective retrieval
 Multimedia retrieval
 Information extraction
 Relevance feedback (Croft, 1995)


Total Information Awareness - DARPA
on the bleeding edge…...
New database technologies
 Database architectures
 Database population
 New search algorithms and data models
 Genysis
 Goal is to produce technology enabling
ultra-large, all-source information
repositories


http://www.darpa.mil/iao/Genisys.htm

Social Issues
Communicating context
 Creating trust/social value
 Inciting cooperation/collaboration
 Privacy tradeoffs: convenience/service or
security/privacy?

References
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