复杂事件处理中多聚合查询共享方法.

Complex event processing technology is a streaming data processing technique that detects specific event sequences or performs statistics on matching events in continuously flowing data. When processing trend aggregation queries with Kleene operators, caching intermediate results is necessary to match an indefinite number of event sequences, thus requiring significant resources from the query system. Exploiting opportunities for sharing among multiple queries, generating shared plans to guide query processing can effectively enhance the processing efficiency of trend aggregation queries. However, existing methods for handling aggregate queries do not dynamically adjust the generated shared plans in real-time to support continuous and efficient processing of complex event detection in response to changes in the execution environment. Addressing these issues, this paper proposed a dynamically updatable method for multiple aggregate query sharing to support the continuous and efficient processing of real-time changing complex event detection. By introducing the shared graph data structure and cost model, this method achieved real-time adjustment of the generated shared plans. And by using the online incremental aggregation execution sharing method, it further enhanced the processing efficiency of event trend aggregation queries with Kleene operators. This paper conducted experiments on both real and simulated datasets, compared the method with other approaches for handling aggregate queries. The results of the experiments indicate that the method effectively reduces query latency and improves the overall processing performance of queries. [ABSTRACT FROM AUTHOR]