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Knowledge Discovery in Databases

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Title: Knowledge Discovery in Databases


1
Knowledge Discovery in Databases Information
Retrieval
University of Texas at Austin School of
information
Knowledge Management Systems Presented
April 29, 2003 By Anne Marie Donovan
2
  • 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)

3
  • 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)

4
  • 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)

5
  • 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)

6
  • 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)

7
  • 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)

8
  • 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)

9
  • 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)

10
  • 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)

11
  • 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)

12
  • 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)

13
  • 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)

14
  • 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)

15
  • 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

16
  • 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

17
  • 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)

18
  • 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
    time-sensitive, multi-media applications

19
  • 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

20
  • 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.)

21
  • 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)

22
  • 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

23
  • 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

24
  • 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 8-27)

25
  • 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)

26
  • 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-to-peer
    databases (data clusters or cubes)
  • Interpolate or extrapolate for missing data
  • (Cherniack et. al., 2001, slides 115-138)

27
  • 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)

28
  • 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, object-oriented data
    (Fayyad, Piatetsky-Shapiro, Smyth, 1996, pp.
    33-34)

29
  • 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)

30
  • 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

31
  • Social Issues
  • Communicating context
  • Creating trust/social value
  • Inciting cooperation/collaboration
  • Privacy tradeoffs convenience/service or
    security/privacy?

32
References
  • Ackerman, M. S. (1998, July). Augmenting the
    organizational memory A field study of Answer
    Garden. ACM Transactions on Information Systems,
    16(3), 203-204. Retrieved March 28, 2003 from
    http//doi.acm.org/10.1145/290159.290160
  • Ackerman, M. S., Malone, T. W. (1990, April).
    Answer Garden A tool for growing organizational
    memory. ACM SIGOIS Bulletin, 11(.2-3), 31-39.
    Retrieved March 28, 2003 from http//doi.acm.org/1
    0.1145/91474.91485
  • Ackerman, M. S., McDonald, D. W. (1996).
    Proceedings of the ACM Conference on
    Computer-Supported Cooperative Work 1996 (CSCW96
    Boston, MA). Retrieved March 28, 2003 from
    http//doi.acm.org/10.1145/240080.240203
  • Boier-Martin, I. M.. (2003, January/February).
    Adaptive graphics. In T. Rhyne (Ed.)
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    rtin/papers/visviewpoints.pdf

33
References
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    Tiware, M. (2000). Using Memex to archive and
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    html
  • Croft, W. B. (1995, November). What do people
    want from information retrieval? The top 10
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    lib/november95/11croft.html
  • DARPA Information Awareness Office. (2003a).
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34
References
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35
References
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36
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