NDLI: SEJ: An Even Approach to Multiway Theta-Joins Using MapReduce

Content Provider	IEEE Xplore Digital Library
Author	Changchun Zhang Jing Li Lei Wu Meiyan Lin Weiqing Liu
Copyright Year	2012
Abstract	Data analyzing and processing are important tasks in cloud computing. The MapReduce framework has been increasingly used to analyze large-scale data over large clusters. Compared with parallel relational database, it has the advantages of excellent scalability and good fault tolerance. However, its performance is not as good as that of parallel relational database. How to efficiently implement join operation using MapReduce is an attractive point to which researchers have been paying attention. Multiway equi-joins and two-way theta-joins using MapReduce have been solved recently. In this paper, we introduce a communication cost model to evaluate multiway theta-joins for the first time and propose a randomized algorithm Strict-Even-Join to solve it. Our algorithm only requires cardinality of input datasets and guarantees the data is distributed across reducers when input datasets are skew. The results of three experiments we have conducted show that our approach is feasible.
Starting Page	73
Ending Page	80
File Size	254607
Page Count	8
File Format	PDF
ISBN	9781467330275
DOI	10.1109/CGC.2012.9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Query Optimization Data analysis Scalability Computational modeling Multiway Theta-Joins Relational databases Educational institutions Skew Optimization MapReduce
Content Type	Text
Resource Type	Article

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SEJ: An Even Approach to Multiway Theta-Joins Using MapReduce