Determining the membership values to optimize retrieval in a fuzzy relational database

Authors:
Shweta Sanghi
Affiliations:
Virginia Commonwealth University, Richmond, VA
Venue:
Proceedings of the 44th annual Southeast regional conference
Year:
2006

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Abstract

A fuzzy data model is able to represent ambiguities in data values as well as impreciseness in the associations among them. In these systems, imprecise information can be stored using fuzzy linguistic terms (e.g. young, big) that are used frequently in daily life. Although these words are ambiguous, community agreement can be reached as to their meaning. A Fuzzy Relational Database extends a normal relational database by adding fuzzy data and membership functions. A membership function is a mathematical function that defines the degree of an element's membership in a fuzzy set. This research aims to determine the best way of setting the membership values given feedback elicited from the community. It is proposed to construct membership values using the Direct Rating method. This approach subscribes to the point of view that fuzziness arises from subjective vagueness. The Direct Rating method is then be compared with the Random Method.