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Dynamically creating indices for two million cases: A real world problem

  • J. Daengdej
  • D. Lukose
  • E. Tsui
  • P. Beinat
  • L. Prophet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1168)

Abstract

Efficiently indexing and retrieving cases from a very large case library are major concerns when building a Case-Based Reasoning (CBR) system. Most CBR research has focused on representation of cases, how to identify features that should be used for retrieval; and similarity measurement between values of attributes. In this paper, we propose a method for dynamically creating indices, and, also different similarity-measurement methods for different types of attributes. We also discuss the use of a relational database for representing cases, taxonomy knowledge, and spatial information. Our real world problem domain consists of 2 million incomplete insurance cases, with 30 different attributes. Even though all of these are valid cases, only 10 percent of these policies have lodged claims. These situations create a very complex case base for reasoning and problem solving. In response to this complexity, the approach adopted in building our CBR system involves a considerable amount of statistical pre-analysis of the contents of the case base to generate domain knowledge that could be used by the “Dynamic Index Creation Mechanism”. The main contribution of this paper is in describing the techniques used in our CBR system to dynamically create indices for the purpose of effective case retrieval.

Keywords

Case-Based Reasoning Indexing Retrieval Relational Database 

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References

  1. 1.
    Aamodt, A., and Plaza, E. Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches,. AI-Communications, Vol. 7, No. 1, 1994.Google Scholar
  2. 2.
    Allen, J.R.C., Patterson, D.W.R., Mulvenna, M.D., and Hughes, J.G. Integration of Case Based Retrieval with a Relational Database System in Aircraft Technical Support, Proceedings of the First International Conference, (ICCBR-95), Springer, October, 1995.Google Scholar
  3. 3.
    Allenmang, D. Combining Case-Based Reasoning and Task-Specific Architectures, IEEE Expert, pp. 24–34. October, 1994.Google Scholar
  4. 4.
    Auriol, E., Manago, M., Althoff, K.D., Wess, S., and Dittrich, S. Integrating Induction and Case-Based Reasoning Methodological Approach and First Evaluation, Proceedings of the Second European Workshop on Case-Based Reasoning (EWCBR 94), Springer, 1994.Google Scholar
  5. 5.
    Barletta, R. A Hybrid Indexing and Retrieval Strategy for Advisory CBR systems Built with ReMind, Proceedings of the Second European Workshop on Case-Based Reasoning (EWCBR 94), Springer, 1994.Google Scholar
  6. 6.
    Bhatta, S. and Ram, A. Learning Indices for Schema Selection. Proceedings of the Fourth Florida Artificial Intelligence Research Symposium, pp. 226–231, Coca Beach, Florida, April, 1991.Google Scholar
  7. 7.
    Cheeseman, P., and Stutz, J. Bayesian Classification (AutoClass): Theory and Results, Advances in Knowledge Discovery and Data Mining, Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, Eds. The AAAI Press, Menlo Park, 1995.Google Scholar
  8. 8.
    Colston, L. SQL Plus: User's Guide and Reference (Version 3.0), Oracle Corporation, USA, 1989.Google Scholar
  9. 9.
    Fox, S., and Leake, D.B. Learning to Refine Indexing by Introspective Reasoning, Proceedings of the First International Conference on Case-Based Reasoning, Springer, 1995.Google Scholar
  10. 10.
    Goss, K. Preselection Strategies for Case-Based Classification, Proceedings of the 18th German Annual Conference on Artificial Intelligence, Springer, 1994.Google Scholar
  11. 11.
    Hammond, K.J. Case-Based Planning, Academic Press, 1989.Google Scholar
  12. 12.
    Hansen, J.V., Meservy, R.D., and Wood, L.E. Indexing Tree and Pruning Concepts to Support Case-Based Reasoning, International Journal on Management Science, Pergamon, Vol. 22, No. 4, pp. 361–369, 1994.Google Scholar
  13. 13.
    Jeng, B.C., and Liang, T-P. Fuzzy Indexing and Retrieval in Case-Based Systems, Expert Systems with Applications, Vol. 8, No. 1, pp. 135–142, 1995.Google Scholar
  14. 14.
    Kang, B.H., and Compton, P. A Maintenance Approach to Case, Proceedings of the Second European Workshop on Case-Based Reasoning (EWCBR 94), Springer, 1994.Google Scholar
  15. 15.
    Kolodner, J. Case-Based Reasoning, Morgan Kaufmann. 1993.Google Scholar
  16. 16.
    Mizoguchi, R., and Hiroshi, M. Expert Systems Research in Japan, IEEE Expert, pp. 15–23, August, 1995.Google Scholar
  17. 17.
    Mood, A., Graybill, F., and Boes, D. Introduction to the Theory of Statistics, McGraw-Hill, 1974.Google Scholar
  18. 18.
    PW. Technology Forecast: 1996, Price Waterhouse World Firm Service BV, p. 460, 1995.Google Scholar
  19. 19.
    Reategui, E.B. and Campbell, J. A Classification System for Credit Card Transactions, Proceedings of the Second European Workshop on Case-Based Reasoning (EWCBR 94), Springer, 1994.Google Scholar
  20. 20.
    Shimazu, H., Kitano, H., and Shibata, A. Retrieving Cases from Relational Data-Bases: Another Stride Towards Corporate-Wide Case-Base Systems, Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI-93), Vol. 2, Chambery, France, pp. 909–914, 1993.Google Scholar
  21. 21.
    Sowa, J. F. Conceptual Structure: Information Processing in Mind and Machine, Addison Wesley, Reading, Mass., 1984.Google Scholar
  22. 22.
    Sycara, K. Using Case-Based Reasoning for Plan Adaptation and Repair, Proceedings of the Case-Based Reasoning Workshop, DARPA. Clearwater Beach, Florida. Morgan Kaufmann, pp. 425–434, 1988.Google Scholar
  23. 23.
    Takahashi, M., Oono, J-I., and Saitoh, K. Reusing Makes It Easier: Manufacturing Process Design by CBR with Knowledge Ware, IEEE Expert, pp. 74–80, December, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • J. Daengdej
    • 1
  • D. Lukose
    • 1
  • E. Tsui
    • 2
  • P. Beinat
    • 2
  • L. Prophet
    • 2
  1. 1.Distributed Artificial Intelligence Center Department of Mathematics, Statistics and Computing ScienceUniversity of New EnglandArmidaleAustralia
  2. 2.Expert Systems GroupContinuum (Australia) Ltd.North SydneyAustralia

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