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Big Graph Analytics Systems

Published: 26 June 2016 Publication History

Abstract

In recent years we have witnessed a surging interest in developing Big Graph processing systems. To date, tens of Big Graph systems have been proposed. This tutorial provides a timely and comprehensive review of existing Big Graph systems, and summarizes their pros and cons from various perspectives. We start from the existing vertex-centric systems, which which a programmer thinks intuitively like a vertex when developing parallel graph algorithms. We then introduce systems that adopt other computation paradigms and execution settings. The topics covered in this tutorial include programming models and algorithm design, computation models, communication mechanisms, out-of-core support, fault tolerance, dynamic graph support, and so on. We also highlight future research opportunities on Big Graph analytics.

References

[1]
R. Cheng, J. Hong, A. Kyrola, Y. Miao, X. Weng, M. Wu, F. Yang, L. Zhou, F. Zhao, and E. Chen. Kineograph: taking the pulse of a fast-changing and connected world. In EuroSys, pages 85--98, 2012.
[2]
W. Han, Y. Miao, K. Li, M. Wu, F. Yang, L. Zhou, V. Prabhakaran, W. Chen, and E. Chen. Chronos: a graph engine for temporal graph analysis. In EuroSys, pages 1:1--1:14, 2014.
[3]
S. Hong, H. Chafi, E. Sedlar, and K. Olukotun. Green-marl: a DSL for easy and efficient graph analysis. In ASPLOS, pages 349--362, 2012.
[4]
U. Kang, H. Tong, J. Sun, C. Lin, and C. Faloutsos. GBASE: a scalable and general graph management system. In SIGKDD, pages 1091--1099, 2011.
[5]
U. Kang, C. E. Tsourakakis, and C. Faloutsos. PEGASUS: A peta-scale graph mining system. In ICDM, pages 229--238, 2009.
[6]
D. Nguyen, A. Lenharth, and K. Pingali. A lightweight infrastructure for graph analytics. In SOSP, pages 456--471, 2013.
[7]
A. Quamar, A. Deshpande, and J. Lin. Nscale: Neighborhood-centric analytics on large graphs. PVLDB, 7(13):1673--1676, 2014.
[8]
J. Seo, S. Guo, and M. S. Lam. Socialite: Datalog extensions for efficient social network analysis. In 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, April 8--12, 2013, pages 278--289, 2013.
[9]
Y. Tian, A. Balmin, S. A. Corsten, S. Tatikonda, and J. McPherson. From "think like a vertex" to "think like a graph". PVLDB, 7(3):193--204, 2013.
[10]
W. Xie, Y. Tian, Y. Sismanis, A. Balmin, and P. J. Haas. Dynamic interaction graphs with probabilistic edge decay. In ICDE, pages 1143--1154, 2015.
[11]
W. Xie, G. Wang, D. Bindel, A. J. Demers, and J. Gehrke. Fast iterative graph computation with block updates. PVLDB, 6(14):2014--2025, 2013.
[12]
D. Yan, J. Cheng, Y. Lu, and W. Ng. Blogel: A block-centric framework for distributed computation on real-world graphs. PVLDB, 7(14):1981--1992, 2014.

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cover image ACM Conferences
SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data
June 2016
2300 pages
ISBN:9781450335317
DOI:10.1145/2882903
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2016

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Author Tags

  1. graph
  2. graphlab
  3. platform
  4. pregel
  5. system
  6. vertex-centric

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SIGMOD/PODS'16
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SIGMOD/PODS'16: International Conference on Management of Data
June 26 - July 1, 2016
California, San Francisco, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

View all
  • (2024)A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and InteractionProceedings of the ACM on Management of Data10.1145/36393152:1(1-29)Online publication date: 26-Mar-2024
  • (2024)Balanced parallel triangle enumeration with an adaptive algorithmDistributed and Parallel Databases10.1007/s10619-023-07437-x42:1(103-141)Online publication date: 1-Mar-2024
  • (2023)Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph QueriesACM Computing Surveys10.1145/360493256:2(1-40)Online publication date: 15-Sep-2023
  • (2023)Optimizing Tensor Computations: From Applications to Compilation and Runtime TechniquesCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589407(53-59)Online publication date: 4-Jun-2023
  • (2022)Fregel: a functional domain-specific language for vertex-centric large-scale graph processingJournal of Functional Programming10.1017/S095679682100027732Online publication date: 20-Jan-2022
  • (2022)Data Management in Machine Learning SystemsundefinedOnline publication date: 26-Feb-2022
  • (2021)Parallel mining of large maximal quasi-cliquesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00712-231:4(649-674)Online publication date: 26-Nov-2021
  • (2020)BAD to the bone: Big Active Data at its coreThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-020-00616-729:6(1337-1364)Online publication date: 23-May-2020
  • (2019)Data Management in Machine Learning SystemsSynthesis Lectures on Data Management10.2200/S00895ED1V01Y201901DTM05714:1(1-173)Online publication date: 25-Feb-2019
  • (2019)Scaling-Out Longitudinal Clinical Analytics with Dataflow Processing2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PERCOMW.2019.8730775(328-333)Online publication date: Mar-2019
  • Show More Cited By

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