AN EFFECTIVE PROPOSAL FOR REVEALING OF HIDDEN SET-LEVEL SEMANTICS

In the recent times, difficult SQL queries that are composed by scalar-level functions are formed to get hold of still extremely set-level semantics. These queries are not complex to write but moreover demanding for database engine to optimize, consequently results in expensive evaluation. Our work will highlight on relational data representation and some of the data analytics systems these days are based on considerable designs of parallel computing. We propose to broaden SQL by means of set predicates to manage set-level comparisons and these predicates, are combined by means of grouping, permit selection of dynamic groups by means of comparison among group as well as set of values. Our work will put forward augmenting of SQL by set predicate, to reveal obscured set-level semantics and suggest two approaches to process set predicates a Aggregate function-basis as well as bitmap index-based approach