NDLI: An Efficient Theta-Join Query Processing Algorithm on MapReduce Framework

Content Provider	IEEE Xplore Digital Library
Author	Shih-Ying Chen Tsui-Ping Chang Zhi-Hong Chang
Copyright Year	2012
Abstract	As the rapid development of hardware and network technology, cloud computing has become an important research topic. For applications of large-scale data processing, such as data warehouse, Map Reduce is the most famous platform for parallel data processing in cloud computing. To support the star-join queries in data warehouse, Scatter-Gather-Merge (SGM) proposes an efficient algorithm on the Map Reduce framework. However, SGM supports only the equi-join queries. Nonequi-join queries may cause SGM to fail. In this paper, we propose a method to cope with theta-join queries, i.e., both equi-join and nonequi-join queries. Our proposed method uses a novel manipulation of keys for partitioning data. The key manipulation matches up the Map Reduce paradigm, and makes theta-join queries workable on the Map Reduce platform. Our experimental results show that the proposed method achieves similar performance to SGM, but our method supports more join-query types. Our method performs even better than SGM in some query types of high data selectivity.
Starting Page	686
Ending Page	689
File Size	312433
Page Count	4
File Format	PDF
ISBN	9781467307673
DOI	10.1109/IS3C.2012.178
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-04
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radio frequency Algorithm design and analysis File systems Operating systems Theta-join queries Filtering algorithms Benchmark testing Data processing Query processing large-scale data MapReduce platform
Content Type	Text
Resource Type	Article

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