Query Processing and Optimization Using Set Predicates.

The scalar-level implication in SQL becomes progressively important to support a new group of operation that needs set-level contrast semantics. That is matching a tuples group with multiple values. Complicated queries of SQL constructed using scalar-level operation are frequently formed to get even simplest set-grade semantics. This type of queries is not only challenging to write and also difficult with regard to database engine optimization, so they may result in expensive evaluation. To overcome this problem, in this paper we suggests to become greater SQL with a new predicates type, set predicates, to come carrying out the as an alternative easily understood semantics and accordingly ease the efficient and direct support. Here we suggested two methods of processing set predicates in this study: An approach based on bitmap index; another approach based on aggregate function. Aggregate functions find their usage for searching tuples which satisfy certain aggregate quality over a group of tuples (in contrast to just calculating an aggregate quality upon a group of tuples). The approach based on bitmap index might be used for eliminating the need for scanning and handling the complete data set (like a table) that ends in significantly speeding up the required query processing. Furthermore, we have devised a probabilistic method that is based on histogram for estimating set predicate selection, in view of optimizing queries having multiple predicates. Also, the experiments have verified the methods accuracy as well as effectiveness of optimizing queries.