Semantic stream query optimization exploiting dynamic metadata

Data stream management systems (DSMS) processing long-running queries over large volumes of stream data must typically deliver time-critical responses. We propose the first semantic query optimization (SQO) approach that utilizes dynamic substream metadata at runtime to find a more efficient query plan than the one selected at compilation time. We identify four SQO techniques guaranteed to result in performance gains. Based on classic satisfiability theory we then design a lightweight query optimization algorithm that efficiently detects SQO opportunities at runtime. At the logical level, our algorithm instantiates multiple concurrent SQO plans, each processing different partially overlapping substreams. Our novel execution paradigm employs multi-modal operators to support the execution of these concurrent SQO logical plans in a single physical plan. This highly agile execution strategy reduces resource utilization while supporting lightweight adaptivity. Our extensive experimental study in the CAPE stream processing system using both synthetic and real data confirms that our optimization techniques significantly reduce query execution times, up to 60%, compared to the traditional approach.