Adaptive optimization for multiple continuous queries

Hong Kyu Park, Won Suk Lee

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Because it operates under a strict time constraint, query processing for data streams should be continuous and rapid. To guarantee this constraint, most previous researches optimize the evaluation order of multiple join operations in a set of continuous queries using a greedy optimization strategy so that the order is re-optimized dynamically in run-time due to the time-varying characteristics of data streams. However, this method often results in a sub-optimal plan because the greedy strategy traces only the first promising plan. This paper proposes a new multiple query optimization approach, Adaptive Sharing-based Extended Greedy Optimization Approach (A-SEGO), that traces multiple promising partial plans simultaneously. A-SEGO presents a novel method for sharing the results of common sub-expressions in a set of queries cost-effectively. The number of partial plans can be flexibly controlled according to the query processing workload. In addition, to avoid invoking the optimization process too frequently, optimization is performed only when the current execution plan is relatively no longer efficient. A series of experiments are comparatively analyzed to evaluate the performance of the proposed method in various stream environments.

Original languageEnglish
Pages (from-to)29-46
Number of pages18
JournalData and Knowledge Engineering
Issue number1
Publication statusPublished - 2012 Jan

All Science Journal Classification (ASJC) codes

  • Information Systems and Management


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