Adaptive two-level optimization for selection predicates of multiple continuous queries
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Query processing for such a data stream should also be continuous and rapid, which requires strict time and space constraints. In order to guarantee these constraints, we have proposed a new scheme called an Attribute Selection Construct (ASC) for an attribute of a data stream in our previous study (Lee and Lee, Information Sciences 178:24162432, 2008). As its optimization technique, this paper proposes the new strategy that determines the evaluation order of multiple ASCs for a given query set at two different levelsmacro and micro levels. Based on the two levels, it also proposes two different strategiesmacro-sequence and hybrid-sequencethat find the optimized full evaluation sequence of all the ASCs. In addition, it provides the adaptive strategy that periodically rearranges the evaluation sequence of multiple ASCs. The performance of the proposed technique is verified by a series of experiments.
selection predicate multiple continuous queries data stream asc micro level macro level macro sequence hybrid sequence adaptive optimization