Neural network-based decision making for large incomplete databases

  • A. R. Hurson
  • B. Jin
  • S. H. Pakzad
Submitted Presentations
Part of the Lecture Notes in Computer Science book series (LNCS, volume 505)


As an extension to the relational algebra, maybe algebra operations have been proposed to handle incomplete information. Such a set of operations allows the user to investigate the potential set of data values (i.e. tuples) to draw his/her own conclusions. However, maybe algebra operations could return nonrelevant data, generate low quality results, and offer low physical performance. Hence, it is appropriate to design a scheme to investigate the results generated by the maybe operations, in order to improve the data quality and performance of large databases. Such a mechanism should be dynamic to adjust itself according to the user's query and the characteristics of the underlying databases. In this paper, an artificial neural network-based decision support system for handling large databases containing incomplete information is proposed. It is a subsystem which learns and constructs a knowledge base to filter out the data that is not of any importance to the user. The network accomplishes the decision-making task in a massively parallel manner. This paper also discusses the implementation of the decision-making network based on the VLSI design of a Basic Neural Unit (BNU). Using a weight-centered design principle, BNU can be expanded and reconfigured to satisfy the requirements of the underlying environment.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Below, R.K., "Designing Appropriate Learning Rules for Connectionist Systems," in Proceedings, IEEE first International Conference on Neural Networks, June 1987, pp. II479–II486.Google Scholar
  2. [2]
    Biskup, J., "A Foundation of Codd's Relational Maybe-Operations," ACM Transaction Database Systems, Vol. 8, No. 4, 1983, pp. 608–636.CrossRefGoogle Scholar
  3. [3]
    Burr, D.J., "A Neural Network Digit Recognizer," in Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, 1986, pp. 1621–1625.Google Scholar
  4. [4]
    Codd, E.F., "Extending the Database Relational Model to Capture Meaning," ACM Transactions on Database Systems, Vol. 4, No. 4, 1979, pp. 397–434.CrossRefGoogle Scholar
  5. [5]
    Codd, E.F., "Missing Information (Applicable and Inapplicable) in Relational Databases," SIGMOD Record, Vol. 15, No. 4, 1986, pp. 53–78.CrossRefGoogle Scholar
  6. [6]
    Grant, J., "Null Values in a Relational Database," Information Processing Lett. 6 (1977) 156–157.CrossRefGoogle Scholar
  7. [7]
    Jin, B., and Hurson, A.R., “Neural Network-Based Decision Support For Incomplete Database Systems,” Proceedings of Analysis of Neural Net Applications Conference, ANNA-91, May 1991, To appear.Google Scholar
  8. [8]
    Hurson, A.R., Miller, L.L. and Pakzad, S.H., "Incomplete Information and the Join Operation in Database Machines," Proceedings of Fall Joint Computer Conference, 1987, pp. 436–443.Google Scholar
  9. [9]
    Hurson, A.R., and Miller, L.L., "Database Machine Architecture for Supporting Incomplete Information," Journal of Computer System Science and Engineering, Vol. 2, No. 3, 1987, pp. 107–116.Google Scholar
  10. [10]
    Jin, B., and Raggad, B., "A Reconfigurable Architecture for A VLSI Implementation of Artificial Neural Networks," Proceedings of 1990 International Neural Network Conference (INNC 90), Paris, July 1990, pp. 665–668.Google Scholar
  11. [11]
    Kohonen, T., "State of the Art in Neural Computing," In Proceedings, IEEE First International Conference on Neural Networks, June 1987, pp. 179–190.Google Scholar
  12. [12]
    Lendaris, G.G., “Neural Networks, Potential Assistants To Knowledge Engineers”, The Journal of Knowledge Engineering, Vol. 1, No. 3, Dec. 1988, pp 7–18.Google Scholar
  13. [13]
    Lippermann, R.P., "An Introduction to Computing with Neural Nets," IEEE ASSP Magazine, April 1987, pp. 4–22.Google Scholar
  14. [14]
    Lipski, W., "On Semantic Issues Connected with Incomplete Information Databases, ACM Transactions on Database Systems, Vol. 4, No. 3, 1979, pp. 262–296.CrossRefGoogle Scholar
  15. [15]
    Pakzad, S.H., Hurson, A.R., and Miller, L.L., “Maybe Algebra and Incomplete Data in Database Machine ASLM,” Journal of Database Technology, 1990.Google Scholar
  16. [16]
    Rumelhart, D.E., Hinton, G.E., and Williams, R.J., "Learning Internal Representations by Error Propagation," in Parallel Distributed Processing (PDP): Explorations in the Microstructure of Cognition, Vol. I: Foundations, MIT Press, Cambridge, Massachusetts, 1986, pp. 318–362.Google Scholar
  17. [17]
    Ullman, J.D., Principles of Database Systems, 2nd Edition, Computer Science Press, Rockville, MD 1982.Google Scholar
  18. [18]
    Widrow, B., Winter, R.G., and Baxter, R.A., "Learning Phenomena in Layered Neural Networks," in Proceedings, IEEE first International Conference on Neural Networks, June 1987, pp. II411–II429.Google Scholar
  19. [19]
    Zaniolo, C., "Relational views in a database system: support for queries," IEEE COMPSAC, 1977, pp. 267–275.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • A. R. Hurson
    • 1
  • B. Jin
    • 1
  • S. H. Pakzad
    • 1
  1. 1.Department of Electrical and Computer EngineeringThe Pennsylvania State UniversityUniversity Park

Personalised recommendations