Advertisement

Separating the cases from the data: Towards more flexible case-based reasoning

  • Mike Brown
  • Ian Watson
  • Nick Filer
Scientific Sessions
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1010)

Abstract

The number of successful, small-scale and purpose-built applications of CBR is growing rapidly. However, CBR has so far not been widely used as a methodology for reusing the large-scale data repositories typically maintained by a corporation. To facilitate this, cases must no longer be considered as concretely represented at the data level, but as virtual views of the underlying data. This paper argues that the basic requirement to support virtual cases are mapping functions between different data representations. It is argued that the use of mapping functions can increase flexibility in a number of ways. Multiple CBR applications can exploit a single database. Similarly, a single case representation can span multiple databases. Support for communication between different CBR applications as well as the evolution of case representation within a single application are also catered for by the same methodology. The paper provides reference to related work on database systems, with respect to the issues of mapping function implementation and management.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Special Issue on Heterogeneous Databases. ACM Computing Surveys, 22(3), September 1990.Google Scholar
  2. 2.
    Special Issue on Heterogeneous Distributed Database Systems. Computer, 24(12), December 1991.Google Scholar
  3. 3.
    R Alterman and M Wentworth. Determining the Important Features of a Case. In DARPA CBR Workshop, pages 197–202. Morgan Kaufmann, 1989.Google Scholar
  4. 4.
    K-D Althoff, E Auriol, R Barletta, and M Manago. A Review of Industrial Case-Based Reasoning Tools, AI Intelligence, PO Box 95, Oxford, OX2 7XL, 1995. ISBN no 1 898804 01 X.Google Scholar
  5. 5.
    S Andreas, G Schlageter, and S Kirn. Problem Solving in Federative Environments: The FRESCO Concept of Cooperative Agents. In The New Generation of Information Systems: From Data to Knowledge. Springer-Verlag, 1992.Google Scholar
  6. 6.
    K D Ashley. Indexing and Analytical Models. In DARPA CBR Workshop, pages 197–202. Morgan Kaufmann, 1989.Google Scholar
  7. 7.
    M Brown. Case-Based Reasoning: Principles and Potential. AI Intelligence, PO Box 95, Oxford, OX2 7XL, 1992.Google Scholar
  8. 8.
    M Brown. Generic Operators for Schema-to-Schema Mappings. Technical Report JCF/MAN/111-05/31-Mar-95, The Uni. of Manchester, 1995.Google Scholar
  9. 9.
    M Brown, Z Moosa, N Filer, J Heaton, and J Pye. Close Integration of a CAD Vendor's Framework into the Jessi-Common-Frame Using a Flexible and Adaptable Procedural Interface, In Proc. of The Int. Workshop on Concurrent/Simultaneous Engineering Frameworks and Applications, Lisboa, Portugal, 1995.Google Scholar
  10. 10.
    C S dos Santos, S Abiteboul, and C Delobel. Virtual Schemas and Bases. Lecture Notes in Computer Science: Proc. of EDBT 94, (779):81–94, 1994.Google Scholar
  11. 11.
    D C Edelson. When Should a Cheetah Remind You of a Bat? Reminding in Case-based Teaching. In Proc. of AAAI-92, pages 667–672, 1992.Google Scholar
  12. 12.
    B Falkenhainer, K D Forbus, and D Gentner. The Structure Mapping Engine: Algorithms and Examples. Artificial Intelligence, 41(1):1–63, 1989.Google Scholar
  13. 13.
    N Filer, M Brown, and Z Moosa. Integrating CAD Tools into a Framework Environment Using a Flexible and Adaptable Procedural Interface. In Proc. of EURO-DAC '94, pages 200–205, Grenoble, 1994. IEEE-CS Press.Google Scholar
  14. 14.
    A K Goel, J L Kolodner, M Pearce, and R Billington. Towards a Case-Based Tool for Aiding Conceptual Design Problem Solving. In DARPA CBR Workshop, pages 109–120, Washington, D.C., 1991.Google Scholar
  15. 15.
    K J Hammond. Explaining and Repairing Plans That Fail. Artificial Intelligence., 45:173–228, 1990.Google Scholar
  16. 16.
    S Mir. Heuristic Reasoning for an Automatic Commonsense Understanding of Logic Electronic Design Specifications. PhD thesis, The Uni. of Manchester, 1993.Google Scholar
  17. 17.
    M J Pazzani. Indexing Strategies for Goal Specific Retrieval of Cases. In DARPA CBR Workshop, pages 31–35. Morgan Kaufmann, 1989.Google Scholar
  18. 18.
    P K C Pun. Knowledge-Based Applications = Knowledge Base + Mappings + Application. PhD thesis, The Uni. of Manchester, 1991.Google Scholar
  19. 19.
    M P Reddy, B E Prasad, P G Reddy, and A Gupta. A Methodology for Integration of Heterogeneous Databases. IEEE Transactions on Knowledge and Data Engineering, 6(6):920–933, December 1994.Google Scholar
  20. 20.
    H Shimazu, H Kitano, and A Shibata. Retrieving Cases from Relational Data-Bases: Another Stride Towards Corporate-Wide Case-Based Systems. In Proc. of IJCAI-93, pages 909–914, Chambéry, 1993. Morgan Kaufmann.Google Scholar
  21. 21.
    J E Vargas and S Raj. Developing Maintainable Expert Systems Using Case-Based Reasoning. Expert Systems, 10(iv):219–225, 1993.Google Scholar
  22. 22.
    I Watson and F Marir. Case-Based Reasoning: A Review. The Knowledge Engineering Review, 9(4):327–54, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Mike Brown
    • 1
  • Ian Watson
    • 2
  • Nick Filer
    • 1
  1. 1.Department of Computer ScienceThe University of ManchesterManchesterUK
  2. 2.Department of SurveyingUniversity of SalfordSalfordUK

Personalised recommendations