Evaluation of pattern matching workloads in graph analysis systems

Seokyong Hong; Sangkeun Lee; Seung Hwan Lim; Sreenivas R. Sukumar; Ranga Raju Vatsavai

doi:10.1145/2907294.2907305

Evaluation of pattern matching workloads in graph analysis systems

Seokyong Hong, Sangkeun Lee, Seung Hwan Lim, Sreenivas R. Sukumar, Ranga Raju Vatsavai

Discrete Algorithms

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, a number of emerging graph analysis systems assume different graph data models, and support different query interface and serialization formats. Such diversity, combined with a lack of comparisons, makes it complicated to understand the trade-offs between different systems and the graph operations for which they are designed. This study presents an evaluation of graph pattern matching capabilities of six graph analysis systems, by extending the Lehigh University Benchmark to investigate the degree of effectiveness to perform the same operation over the same graph in various graph analysis systems. Through the evaluation, this study reveals both quantitative and qualitative findings.

Original language	English
Title of host publication	HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing
Publisher	Association for Computing Machinery, Inc
Pages	263-266
Number of pages	4
ISBN (Electronic)	9781450343145
DOIs	https://doi.org/10.1145/2907294.2907305
State	Published - May 31 2016
Event	25th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2016 - Kyoto, Japan Duration: May 31 2016 → Jun 4 2016

Publication series

Name	HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing

Conference

Conference	25th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2016
Country/Territory	Japan
City	Kyoto
Period	05/31/16 → 06/4/16

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Funders	Funder number
DOE Public Access Plan
United States Government
U.S. Department of Energy

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10.1145/2907294.2907305

Cite this

Hong, S., Lee, S., Lim, S. H., Sukumar, S. R., & Vatsavai, R. R. (2016). Evaluation of pattern matching workloads in graph analysis systems. In HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing (pp. 263-266). (HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/2907294.2907305

Hong, Seokyong ; Lee, Sangkeun ; Lim, Seung Hwan et al. / Evaluation of pattern matching workloads in graph analysis systems. HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing. Association for Computing Machinery, Inc, 2016. pp. 263-266 (HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing).

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abstract = "Graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, a number of emerging graph analysis systems assume different graph data models, and support different query interface and serialization formats. Such diversity, combined with a lack of comparisons, makes it complicated to understand the trade-offs between different systems and the graph operations for which they are designed. This study presents an evaluation of graph pattern matching capabilities of six graph analysis systems, by extending the Lehigh University Benchmark to investigate the degree of effectiveness to perform the same operation over the same graph in various graph analysis systems. Through the evaluation, this study reveals both quantitative and qualitative findings.",

author = "Seokyong Hong and Sangkeun Lee and Lim, {Seung Hwan} and Sukumar, {Sreenivas R.} and Vatsavai, {Ranga Raju}",

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Hong, S, Lee, S , Lim, SH, Sukumar, SR & Vatsavai, RR 2016, Evaluation of pattern matching workloads in graph analysis systems. in HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing. HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing, Association for Computing Machinery, Inc, pp. 263-266, 25th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2016, Kyoto, Japan, 05/31/16. https://doi.org/10.1145/2907294.2907305

Evaluation of pattern matching workloads in graph analysis systems. / Hong, Seokyong; Lee, Sangkeun ; Lim, Seung Hwan et al.
HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing. Association for Computing Machinery, Inc, 2016. p. 263-266 (HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Hong S, Lee S , Lim SH, Sukumar SR, Vatsavai RR. Evaluation of pattern matching workloads in graph analysis systems. In HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing. Association for Computing Machinery, Inc. 2016. p. 263-266. (HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing). doi: 10.1145/2907294.2907305

Evaluation of pattern matching workloads in graph analysis systems

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