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<p><b>Abstract</b>—We propose a new measure of fuzzy equality comparison based on the similarity of possibility distributions. we define a type of fuzzy equi–join based on the new fuzzy equality comparison, and allow threshold values to be associated with predicates of the join condition. A sort–merge join algorithm based on a partial order of intervals is used to evaluate the fuzzy equi-join. In order for the evaluation to be efficient, we identify various mappings, called fuzzy equality (FE) indicators, that will determine appropriate intervals for fuzzy data with different characteristics. Experiment results from our preliminary simulation of the algorithm show a significant improvement of efficiency when FE indicators are used with the sort–merge join algorithm.</p>
Fuzzy databases, fuzzy equi–join, fuzzy equality indicator, algorithm, performance.

W. Zhang and K. Wang, "An Efficient Evaluation of a Fuzzy Equi-Join Using Fuzzy Equality Indicators," in IEEE Transactions on Knowledge & Data Engineering, vol. 12, no. , pp. 225-237, 2000.
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