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In many applications data values are inherently uncertain. This includes moving-objects, sensors and biological databases here has been recent interest in the development of database management systems that can handle uncertain data. Some proposals for such systems include attribute values that are uncertain. In particular, an attribute value can be modeled as a range of possible values, associated with a fuzzy density function. Joins are arguably the most important relational operators. Poor implementations are tantamount to computing the Cartesian product of that join multiple relations have not been addressed in earlier work despite the significance of joins in fuzzy temporal databases. In this paper we address join over uncertain data. We propose a semantics for the join operation, define fuzzy operators over uncertain data, and propose join algorithms that provide efficient execution of fuzzy joins. The paper focuses on an important class of joins termed inequality predicates prevalent that avoid some of the semantic complexities of dealing with uncertain data. These techniques facilitate pruning with little space and time overhead, and are easily adapted to most join algorithms. We address this need for efficient join evaluation in fuzzy temporal databases. Our purpose is that we first survy previously proposed fuzzy temporal join operators and we then address evaluation algorithms, comparing the applicability of various algorithms to the fuzzy temporal join operators and describing a performance study involving algorithms for important operator.