Adaptive Continuous Query Reoptimization over Data Streams

A data stream is a series of massive unbounded tuples continuously generated at a rapid rate. Continuous queries for data streams should be processed continuously. so that it strict time constraint is required. In most previous research studies, in order to guarantee this constraint, the evaluation order of join predicates ill a continuous query is optimized Using a greed), strategy. However, because a greedy strategy traces only the first promising plan, it often finds a suboptimal plan. To reduce the possibility of producing a suboptimal plan. in this paper, we propose an improved scheme, k-Extended Greedy Algorithm (k-EGA). that simultaneously examines a set of promising plans and reoptimize an execution plan adaptively. The number of promising plans is flexibly controlled by a user-defined range variable. The scheme verifies the performance of the Current plan periodically. If the plan is no longer efficient, a newly optimized plan is generated. The performance of the proposed scheme is verified through various experiments to identify its various characteristics.