상세정보
학술지
Scalable and Adaptive Graph Querying with MapReduce
- 저자
- 김송현, 이경하, 송인철, 최혜봉, 이윤준
- 발행일
- 201309
- 출처
- IEICE Transactions on Information and Systems, v.E96.D no.9, pp.2126-2130
- ISSN
- 0916-8532
- 출판사
- 일본, 전자정보통신학회 (IEICE)
- 협약과제
- 13PR1800, 빅데이터 활용을 위한 지식 자산(Knowledge Base) 구축 및 실시간 Linked Data 응용기술 개발, 조기성
- 초록
- 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 제안 키워드
- Adaptive graph, Data graphs, Distributed Graph querying, Feature size, Feature-based, Graph Pattern Matching, Information and communication, MapReduce framework, conventional algorithms