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Neighborhood/conceptual query answering with imprecise/incomplete data

  • Show-Jane Yen
  • Arbee L. P. Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 823)

Abstract

The evolution of database systems tends to the development of higher degree of user-friendliness such that the system can be directly handled by nonprofessionals. In order to reach this goal, the database system needs to provide a query language by which queries can be specified conceptually. Also, the query condition may be relaxed such that information within a certain semantic distance to the exact answer can be obtained. Moreover, the real-world information is usually imprecise and incomplete. It is therefore important to store imprecise and incomplete information in a database, and to manipulate this information accordingly.

In this paper, we provide a conceptual query language by which fuzzy query conditions and neighborhood query conditions can be specified. Query processing strategies for these two query conditions are proposed considering imprecise and incomplete information. A domain concept hierarchy is constructed on top of a numerical domain to handle imprecise data, while dependencies between database attributes are derived for incomplete information. An application of the techniques for processing queries under network partitioning is also discussed.

Keywords

Relational Database Query Processing Condition Part Semantic Distance Semantic Association 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Buckles, B.P. and Petry, F.E., A Fuzzy Representation of Data for Relational Databases, Fuzzy Set and Systems, 1982, pp.213–226.Google Scholar
  2. 2.
    Buckles, B.P. and Petry, F.E., Fuzzy Databases and their Applications, Fuzzy Information and Decision Processes, 1982, pp.361–371.Google Scholar
  3. 3.
    Ceri, S. and Pelagatti, G., Distributed Database Principles and Systems, New York, NY: McGraw-Hill, 1984.Google Scholar
  4. 4.
    Chu, W. and Chen, Q., A Structured Approach for Cooperative Query Answering, To Appear in IEEE Trans. on Knowledge and Data Engineering, 1993.Google Scholar
  5. 5.
    Chu, W. and Chen, Q., Neighborhood and Associative Query Answering, To Appear in Journal of Intelligent Information Systems, 1993.Google Scholar
  6. 6.
    Chu, W., Chen, Q. and Page, T., Cobase: Cooperative Distributed Databases, Invited Paper, Proc. of the 6th Brazilian Symposium on Databases, Brazil, 1991.Google Scholar
  7. 7.
    Chu, W., Chen, Q. and Lee, R., Cooperative Query Answering via Type Abstraction Hierarchy, In Cooperating Knowledge Based Systems, 1990, S.M. Deen eds, Elsevier Science Publishing Co. Inc, 1991.Google Scholar
  8. 8.
    Chu, W., et al., Design Considerations of a Fault Tolerant Distributed Database System by Inference Technique, Extended Abstract, Proceedings of PARBASE-90, March 6–8, 1990, Miami.Google Scholar
  9. 9.
    Chu, Wesley and Hwang, Andy, Inference Techniques for a Fault Tolerant Distributed Database System, Extended Abstract, Proceedings of PARBASE-90, March 6–8, 1990, Miami.Google Scholar
  10. 10.
    Chu, W., Hwang, A., Chen, Q. and Lee, R.C., An Inference Technique for Distributed Query Processing in a Partitioned Network, Technical Report, CSD-900005, February, 1990, UCLA.Google Scholar
  11. 11.
    Chu, W., Lee, R.C. and Chen, Q., Fault tolerant distributed database system via data inference, Proceedings of the Ninth Symposium on Reliable Distributed Systems, October, 1990.Google Scholar
  12. 12.
    Chu, W., Lee R.C. and Chiang, K., Capture database semantics by rule induction, Technical Report CSD-900013, May, 1990.Google Scholar
  13. 13.
    Codd, E.F., Extending the database relational model to capture more meaning, ACM Trans. Database Systems, 4(4), 1979, pp.397–434.CrossRefGoogle Scholar
  14. 14.
    El Abbadi, Skeen, D. and Criistian, F., An efficient fault-tolerant protocol for replicated data management, in Proc. 4th ACM SIGACT-SIGMOD Symp. on Principles of Database Systems, 1985, pp.215–229.Google Scholar
  15. 15.
    Garcia-Molina, H. and Abbott, R.K., Reliable Distributed Database Management, Proc. of the IEEE, May, 1987, pp. 601–620.Google Scholar
  16. 16.
    Grant, J., Null values in a Relational Data Base, Inform. Process. Lett., 6, 1977, pp.156–157.CrossRefGoogle Scholar
  17. 17.
    Ichikawa, T. and Hirakawa, M., ARES: A Relational Database with the Capability of Performing Flexible Interpretation of Queries, IEEE Trans. Softw. Eng. SE-12, 5,May 1986, pp.624–634.Google Scholar
  18. 18.
    Imielinski, T. and Lipski, W., Incomplete Information in Relational Databases, JACM, Vol.31(4), 1984, pp.761–791.CrossRefGoogle Scholar
  19. 19.
    Motro, A., Supporting Goal Queries in Relational Databases, Proc. 1st International Conference on Expert Database Systems, 1986, pp.85–96.Google Scholar
  20. 20.
    Motro, A., VAGUE: A User Interface to Relational Databases that Permits Vague Queries, ACM Trans. on Office Information Systems, 1988, pp.187–214.Google Scholar
  21. 21.
    Prad, H., Lipski's Approach to Incomplete Information Databases Restated and Generalized in the Setting of Zadeh's Possibility Theory, Information Systems, 9(1), 1984, pp.27–42.CrossRefGoogle Scholar
  22. 22.
    Prad, H., The Connection between Lipski's Approach to Incomplete Information Data Base and Zadeh's Possibility Theory, Proceedings of the International Conference on Systems Methodology, 5–9, Jan. 1982, pp.402–408.Google Scholar
  23. 23.
    Prad, H. and Testemale, C., Generalizing Database Relational Algebra for the Treatment of Incomplete or Uncertain Information and Vague Queries, Information Sciences, 1984, pp.115–143.Google Scholar
  24. 24.
    Takahashi, T., A Fuzzy Query Language for Relational Databases, IEEE Transactions on Systems, Man and Cybernetics, Vol.21, No.6, November/December, 1991.Google Scholar
  25. 25.
    Zemankava, M. and Kandel, A., Implementing Imprecision in Information Systems, Information Sciences, 1985, pp.107–141.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Show-Jane Yen
    • 1
  • Arbee L. P. Chen
    • 1
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan 300, R.O.C.

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