Efficient Processing of an Aggregate Query Stream in MapReduce


KIPS Transactions on Software and Data Engineering, Vol. 3, No. 2, pp. 73-80, Feb. 2014
10.3745/KTSDE.2014.3.2.73,   PDF Download:

Abstract

MapReduce is a widely used programming model for analyzing and processing Big data. Aggregate queries are one of the most common types of queries used for analyzing Big data. In this paper, we propose an efficient method for processing an aggregate query stream, where many concurrent users continuously issue different aggregate queries on the same data. Instead of processing each aggregate query separately, the proposed method processes multiple aggregate queries together in a batch by a single, optimized MapReduce job. As a result, the number of queries processed per unit time increases significantly. Through various experiments, we show that the proposed method improves the performance significantly compared to a naive method.


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Cite this article
[IEEE Style]
H. J. Choi and K. Y. Lee, "Efficient Processing of an Aggregate Query Stream in MapReduce," KIPS Transactions on Software and Data Engineering, vol. 3, no. 2, pp. 73-80, 2014. DOI: 10.3745/KTSDE.2014.3.2.73.

[ACM Style]
Hyun Jean Choi and Ki Yong Lee. 2014. Efficient Processing of an Aggregate Query Stream in MapReduce. KIPS Transactions on Software and Data Engineering, 3, 2, (2014), 73-80. DOI: 10.3745/KTSDE.2014.3.2.73.