Applications of fuzzy languages and pictorial databases to decision support systems design

  • Authors:
  • Edward T. Lee

  • Affiliations:
  • Memphis State University, Memphis, Tennessee

  • Venue:
  • AFIPS '83 Proceedings of the May 16-19, 1983, national computer conference
  • Year:
  • 1983

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Abstract

The pioneering work of D. T. Lee in developing an approach with major emphasis on database development has had a profound influence on the recent development of decision support systems, as well as on office information systems, database systems, and database machines. This database development approach is a new and powerful approach to decision support systems design methodologies. In this paper the concepts of fuzzy languages and pictorial databases are applied to decision support systems design methodologies. First, fuzzy languages, fuzzy grammars, the classification of fuzzy grammars, derivation chain, degree of acceptance, and equivalence are defined. Operations like intersection, concatenation, Kleene closure, complement, and cardinality are also defined. Second, algebraic representation of the production system is presented and illustrated by examples. The difference between null string and the empty set of string is illustrated. Third, decision support systems involving geometric figures, chromosome images or leukocyte images are presented as illustrative examples. In similarity retrieval from a pictorial database, very often it is desired to find pictures (or feature vectors, histograms, etc.) that are most similar to or most dissimilar to a test picture (or feature vector). Using similarity measures, one can not only store similar pictures logically or physically close to each other to improve retrieval or updating efficiency, one can also use such similarity measures to answer fuzzy queries involving nonexact retrieval conditions. The applications of fuzzy languages and pictorial databases to decision support systems design methodologies offer what appears to be a fertile field for further study. The underlying ideas are interesting and easy for practical application. The results have useful applications in decision support systems, pattern recognition, pictorial information systems, and artificial intelligence.