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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)

Abstract

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.

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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

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