In a fuzzy relational database where a relation is a fuzzy set of tuples and ill-known data are represented by possibility distributions, nested fuzzy queries can be expressed in the Fuzzy SQL language. Although it provides a very convenient way for users to express complex queries, a nested fuzzy query may be very inefficient to process with the naive evaluation method based on its semantics. In conventional databases, nested queries are unnested to improve the efficiency of their evaluation. In this paper, we extend the unnesting techniques to process several types of nested fuzzy queries. An extended merge-join is used to evaluate the unnested fuzzy queries. As shown by both theoretical analysis and experimental results, the unnesting techniques with the extended merge-join significantly improve the performance of evaluating nested fuzzy queries.

Fuzzy databases have been introduced to deal with uncertain or incomplete information in many applications. The efficiency of processing fuzzy queries in fuzzy databases is a major concern. We provide techniques to unnest nested fuzzy queries of two blocks in fuzzy databases. We show both theoretically and experimentally that unnesting improves the performance of nested queries significantly. The results obtained in the paper form the basis for unnesting fuzzy queries of arbitrary blocks in fuzzy databases.>

A new hybrid algorithm is proposed for well-formed path problems including the transitive closure problem. The CPU time for computation is O(ne), and blocking technique is incorporated to reduce the disk I/O cost in disk-resident environment. The new features of the new algorithm are that only parents sets instead of descendant sets are loaded in from disk, and the computation can be parallelized efficiently. Simulation results show that our algorithm is superior to other existing algorithms in sequential computation, and that linear speedup is achieved in parallel computation.>