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Pregelix: dataflow-based big graph analytics

Published: 01 October 2013 Publication History

Abstract

Recently, Google has proposed the Pregel programming model [2] for Big Graph analytics, where application programmers need no knowledge of parallel or distributed systems. Instead, they just need to "think like a vertex" and write a few functions that encapsulate the logic for what one graph vertex does. The vertex-oriented programming model has been found to ease the implementation of distributed graph algorithms to a great extent.

References

[1]
Giraph. http://giraph.apache.org/.
[2]
Grzegorz Malewicz et al. Pregel: a system for large-scale graph processing. In SIGMOD, 2010.
[3]
Vinayak R. Borkar et al. Hyracks: A flexible and extensible foundation for data-intensive computing. In ICDE, 2011.
[4]
Yucheng Low et al. Distributed GraphLab: A framework for machine learning in the cloud. PVLDB, 2012.

Cited By

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  • (2019)Optimizing Declarative Graph Queries at Large ScaleProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300064(1411-1428)Online publication date: 25-Jun-2019
  • (2018)iPregelWorkshop Proceedings of the 47th International Conference on Parallel Processing10.1145/3229710.3229719(1-10)Online publication date: 13-Aug-2018
  • (2018)Review of Graph Processing Frameworks2018 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2018.00144(998-1005)Online publication date: Nov-2018
  • Show More Cited By

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Published In

cover image ACM Conferences
SOCC '13: Proceedings of the 4th annual Symposium on Cloud Computing
October 2013
427 pages
ISBN:9781450324281
DOI:10.1145/2523616
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2013

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  • Research-article

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SOCC '13
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SOCC '13: ACM Symposium on Cloud Computing
October 1 - 3, 2013
California, Santa Clara

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SOCC '13 Paper Acceptance Rate 23 of 114 submissions, 20%;
Overall Acceptance Rate 169 of 722 submissions, 23%

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Cited By

View all
  • (2019)Optimizing Declarative Graph Queries at Large ScaleProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300064(1411-1428)Online publication date: 25-Jun-2019
  • (2018)iPregelWorkshop Proceedings of the 47th International Conference on Parallel Processing10.1145/3229710.3229719(1-10)Online publication date: 13-Aug-2018
  • (2018)Review of Graph Processing Frameworks2018 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2018.00144(998-1005)Online publication date: Nov-2018
  • (2014)Large-scale distributed graph computing systemsProceedings of the VLDB Endowment10.14778/2735508.27355178:3(281-292)Online publication date: 1-Nov-2014
  • (2014)Big Graph AnalyticsProceedings of the 17th International Workshop on Data Warehousing and OLAP10.1145/2666158.2668454(99-101)Online publication date: 7-Nov-2014

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