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Scalable and Adaptive Graph Querying with MapReduce
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- Authors
- Song-Hyon KIM, Kyong-Ha LEE, Inchul SONG, Hyebong CHOI, Yoon-Joon LEE
- Issue Date
- 2013-09
- Citation
- IEICE Transactions on Information and Systems, v.E96.D, no.9, pp.2126-2130
- ISSN
- 0916-8532
- Publisher
- 일본, 전자정보통신학회 (IEICE)
- Language
- English
- Type
- Journal Article
- Abstract
- We address the problem of processing graph pattern matching queries over a massive set of data graphs in this letter. As the number of data graphs is growing rapidly, it is often hard to process such queries with serial algorithms in a timely manner. We propose a distributed graph querying algorithm, which employs feature-based comparison and a filter-and-verify scheme working on the MapReduce framework. Moreover, we devise an efficient scheme that adaptively tunes a proper feature size at runtime by sampling data graphs. With various experiments, we show that the proposed method outperforms conventional algorithms in terms of scalability and efficiency. Copyright © 2013 The Institute of Electronics, Information and Communication Engineers.
- KSP Keywords
- Adaptive graph, Data graphs, Distributed Graph querying, Feature size, Feature-based, Graph Pattern Matching, Information and communication, MapReduce framework, conventional algorithms