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Knowledge Discovery in Databases & Information Retrieval

Knowledge Discovery in Databases & Information Retrieval. University of Texas at Austin School of i nformation. Knowledge Management Systems Presented April 29, 2003 By Anne Marie Donovan. Knowledge Discovery in Databases

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Knowledge Discovery in Databases & Information Retrieval

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  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 metricsto 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/10.1145/91474.91485 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.) Visualization Viewpoints,IEEE Computer Graphics and Application, 23(1), 6-10. Retrieved April 5, 2003 from http://www.research.ibm.com/people/i/imartin/papers/visviewpoints.pdf

  33. References Chakrabarti, S., Srivastava, S., Subramanyam, M., & Tiware, M. (2000). Using Memex to archive and mine community Web browsing experience. A paper presented at the 9th International World Wide Web Conference, Amsterdam, May 15-19, 2000. Retrieved April 12, 2003 from http://www9.org/w9cdrom/98/98.html Croft, W. B. (1995, November). What do people want from information retrieval?: The top 10 research issues for companies that use and sell IR systems. D-Lib Magazine. Retrieved April 5, 2003 from http://sunsite.anu.edu.au/mirrors/dlib/dlib/november95/11croft.html DARPA Information Awareness Office. (2003a). Genysis. Retrieved from the DARPA Information Awareness Office Web site at: http://www.darpa.mil/iao/Genisys.htm DARPA Information Awareness Office. (2003b). Total Information Awareness System. Retrieved from the DARPA Information Awareness Office Web site at: http://www.darpa.mil/iao/TIASystems.htm

  34. References Devarakonda, R. (2001, March). Object-Relational database systems - The road ahead. ACM Crossroads Student Magazine. Retrieved April 12, 2003 from www.acm.org/crossroads/xrds7-3/ordbms.html Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996, November). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34. Retrieved March 03, 2003 from http://wwwhome.cs.utwente.nl/~mpoel/colleges/dwdm/ACM_artikelen/fayyad2.pdf Lee, D., & Hwang, Y. (2002, March 1). Extracting semantic metadata and its visualization. ACM Crossroads Student Magazine. Retrieved March 27, 2003 from www.acm.org/crossroads/xrds7-3/smeva.html Piatetsky-Shapiro, G. (1998, December 4). Data mining and knowledge discovery tools: The next generation. Retrieved February 27, 2003 from kdnuggets.com at http://www.kdnuggets.com/gpspubs/dama-nextgen-98/index.htm

  35. References Rauber, A., Aschenbrenner, A., Witvoet, O., Bruckner, R. M., & Kaiser, M. (2002, December). Uncovering information hidden in Web archives: A glimpse at Web analysis building on data warehouses. D-Lib Magazine, 8(12). Retrieved March 28, 2003 from http://www.dlib.org/dlib/december02/rauber/12rauber.html Rocha, L. M. (2001). TalkMine: A soft computing approach to adaptive knowledge recommendation [Electronic version]. In V. Loia & S. Sessa (Eds.), Studies in fuzziness and soft computing: Vol. 75. Soft computing agents: New trends for designing autonomous systems. (pp. 89-116). New York: Springer. Retrieved March 28, 2003 from http://www.c3.lanl.gov/~rocha/softagents.html Shah, U., Finin, T., Joshi, A., Cost, R. S., & Mayfield, J. (2002, November). Information retrieval on the Semantic Web. Paper presented at The ACM Conference on Information and Knowledge Management , November 2002. Retrieved March 28, 2003 from http://www.csee.umbc.edu/~finin/papers/cikm02/cikm02.pdf

  36. References Smith, M. (2002). Tools for navigating large social cyberspaces. Communications of the ACM, 45(4), 51-55. Retrieved March 28, 2003 from http://delivery.acm.org/10.1145/510000/505272/p51-smith.html?key1=505272&key2=5541680501&coll=GUIDE&dl=GUIDE&CFID=9914049&CFTOKEN=12943474 Whitted, T. (1999, July/August). Draw on the Wall. IEEE Computer Graphics and Applications, 19(4), 6-9. Retrieved April 8, 2003 from ieeeexplore.ieee.org at: http://ieeexplore.ieee.org/iel5/38/16795/00773957.pdf?isNumber=16795&arnumber=773957&prod=JNL&arSt=6&ared=9&arAuthor=Whitted%2C+T. Widom, J. (1995, November). Research problems in data warehousing. Proceedings of the 4th International Conference on Information and Knowledge Management (CIKM). Retrieved March 28, 2003 from http://www.ischool.utexas.edu/~i385tkms/readings/Widom-1995-ResearchProblems.pdf

  37. References Xion, R., & Donath, J. (1999). PeopleGarden: Creating data portraits for users. CHI Letters, 1(1). 37-44. Retrieved April 8, 2003 from http://smg.media.mit.edu/papers/Xiong/pgarden_uist99.pdf

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