An Efficient Approach of Processing Multiple Continuous Queries
An Efficient Approach of Processing Multiple Continuous Queries作者机构：School of Computer Science and Technology Dalian University of Technology Dalian 116024 China School of Computer Science Fudan University Shanghai 201203 China
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2016年第31卷第6期
页 面：1212-1227页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学]
基 金：This work was supported by the National Natural Science Foundation of China under Grant No. 61173160 the National Basic Research 973 Program of China under Grant No. 2015CB358800 and the Scientific Research Program of the Higher Education Institution of Xinjiang Uygur Autonomous Region of China under Grant No. XJEDU2014S087
主 题：data stream streams aggregation query sharing continuous query
摘 要：As stream data is being more frequently collected and analyzed, stream processing systems are faced with more design challenges. One challenge is to perform continuous window aggregation, which involves intensive computation. When there are a large number of aggregation queries, the system may suffer from scalability problems. The queries are usually similar and only differ in window specifications. In this paper, we propose collaborative aggregation which promotes aggregate sharing among the windows so that repeated aggregate operations can be avoided. Different from the previous approaches in which the aggregate sharing is restricted by the window pace, we generalize the aggregation over multiple values as a series of reductions. Therefore, the results generated by each reduction step can be shared. The sharing process is formalized in the feed semantics and we present the compose-and-declare framework to determine the data sharing logic at a very low cost. Experimental results show that our approach offers an order of magnitude performance improvement to the state-of-the-art results and has a small memory footprint.
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