Advertisement

Inductive dependencies and approximate databases

  • Debby Keen
  • Arcot Rajasekar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 752)

Abstract

Query processing in a (relational) database context has been mainly confined to deducing information that is available in the database. The answers given to queries are supported by available data in the database and are computed using the classical operations of select, project and join. When the query cannot be answered using the above operations the database system returns an empty answer. But there are cases where an approximate answer for the query would be desirable instead of no answer from the database. In this paper, we provide one such approximation technique, inductive dependencies, that can be used to enhance conventional relational databases. The approach finds an approximation for a null value in the database, by using similarities, aggregate functions and relationships that are not expressible by functional dependencies. Inductive dependencies can also be applied to heterogeneous databases, where relationships between databases need to be expressed in a concise way.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    W.W. Armstrong. Dependency Structures of Database Relationships. In Proceedings of IFIP 74, pages 580–583. North Holland, 1974.Google Scholar
  2. 2.
    J. Biskup. A Formal Approach to Null Values in Database Relations. In J. Minker and J.M. Nicholas, editors, Advances in Database Theory: Vol. 1, pages 299–341. Plenum, New York, 1981.Google Scholar
  3. 3.
    Y.L. Breitbart, P.L. Olson, and G.R. Thompson. Database Integration in a Distributed Heterogeneous Database System. In Proceedings of the International Conference on Data Engineering, pages 301–310, Washington, DC, February 1986. IEEE Computer Society.Google Scholar
  4. 4.
    E.F. Codd. Missing Information (Applicable and Inapplicable) in Relational Databases. SIGMOD RECORD, 15(4):53–78, 1986.Google Scholar
  5. 5.
    E.F. Codd. Extending the Database Relational Model to Capture More Meaning. ACM Trans. on Database Systems, 4(4):394–434, December 1979.Google Scholar
  6. 6.
    E.F. Codd. A relational model of data for large shared data banks. Comm. ACM, 13(6):377–387, June 1970.Google Scholar
  7. 7.
    C.J. Date. Null Values in Data Base Management, 1982. (Also found in C.J.Date, Relational Database: Selected Writings, 1986, Addison Wesley).Google Scholar
  8. 8.
    C.J. Date. An Introduction to Database Systems. Addison-Wesley, Reading, Mass., 1986.Google Scholar
  9. 9.
    T.R. Davies and S.J. Russell. A Logical Approach to Reasoning by Analogy. In Proceedings of the 10th IJCAI, pages 264–270, 1987.Google Scholar
  10. 10.
    L.G. DeMichiel. Resolving Database Incompatibility: An Approach to Performing Relational Operations over Mismatched Domains. IEEE Transactions on Knowledge and Data Engineering, 1(4):485–493, 1989.Google Scholar
  11. 11.
    R. Fagin. Multivalued Dependencies and a New Form for Relational Databases. ACM Trans. on Database Systems, 2(3), September 1977.Google Scholar
  12. 12.
    G. Grahne. Dependency Satisfaction in Databases with Incomplete Information. In Proceedings of VLDB 84, pages 37–45. Morgan Kaufmann, 1984.Google Scholar
  13. 13.
    J. Grant. Null Values in a Relational Database. Information Processing Letters, 6(5), October 1977.Google Scholar
  14. 14.
    T. Imielinski and W. Lipski. Incomplete Information in Relational Databases. J.ACM, 31(4):761–791, October 1984.Google Scholar
  15. 15.
    W. Lipski. On Databases with Incomplete Information. J.ACM, 28(1):41–70, January 1981.Google Scholar
  16. 16.
    R.S. Michalski, J.G. Carbonell, and T.M. Mitchell, editors. Machine Learning: An AI Approach. Morgan Kaufmann Publishers, 1983.Google Scholar
  17. 17.
    J. Minker and J. Grant. Answering queries in indefinite databases and the null value problem. In P. Kanellakis, editor, Advances in Computing Research, pages 247–267. 1986.Google Scholar
  18. 18.
    A. Motro. Extending the Relational Database Model to Support Goal Queries. In L. Kerschberg, editor, Expert Database Systems, pages 129–150. Benjamin Cummings, 1987.Google Scholar
  19. 19.
    A. Rajasekar. Inductive Dependencies. Technical Report 189-91, University of Kentucky, Lexington, Kentucky, July, 1991.Google Scholar
  20. 20.
    U.Dayal and H.Y. Hwang. View Definition and Generalization for Database Integration in MULTIBASE. In Proceedings of Berkley Workshop on Distributed Data Management and Computer Networks, 1982.Google Scholar
  21. 21.
    J.D. Ullman, editor. Principles of Database and Knowledge-Base Systems. Computer Science Press, Rockville, Maryland, 1988.Google Scholar
  22. 22.
    Y. Vassiliou. Functional Dependencies and Incomplete Information. In Proceedings of VLDB 80, pages 260–269, 1980.Google Scholar
  23. 23.
    R.L. Winkler and W.L. Hays, editors. Statistics: Probability, Inference and Decision. Holt, Rinehart and Winston, New York, New York, 1975.Google Scholar
  24. 24.
    L.A. Zadeh. Fuzzy Sets. Information Control, 8:338–353, 1965.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Debby Keen
    • 1
  • Arcot Rajasekar
    • 1
  1. 1.Department of Computer ScienceUniversity of KentuckyLexington

Personalised recommendations