Journal of Intelligent Information Systems

, Volume 39, Issue 2, pp 317-334

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Adaptive two-level optimization for selection predicates of multiple continuous queries

  • Hyun-Ho LeeAffiliated withDepartment of Non-commissioned officers, Anyang Science University Email author 
  • , Won-Suk LeeAffiliated withDepartment of Computer Science, Yonsei University


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 178:2416–2432, 2008). As its optimization technique, this paper proposes the new strategy that determines the evaluation order of multiple ASC’s for a given query set at two different levels—macro and micro levels. Based on the two levels, it also proposes two different strategies—macro-sequence and hybrid-sequence—that find the optimized full evaluation sequence of all the ASC’s. In addition, it provides the adaptive strategy that periodically rearranges the evaluation sequence of multiple ASC’s. The performance of the proposed technique is verified by a series of experiments.


Data stream Multiple continuous queries Selection predicate ASC Macro level Micro level Macro sequence Hybrid sequence Adaptive optimization