The Future is Big Graphs! A Community View on Graph Processing Systems

Sherif Sakr, Angela Bonifati, Hannes Voigt, Alexandru Iosup, Hsiang-Yun Wu et al.
The Future is Big Graphs! A Community View on Graph Processing Systems
Communications of the ACM , x:1-14, December 2020. [image] [paper]

Information

  • Publication Type: Journal Paper (without talk)
  • Workgroup(s)/Project(s): not specified
  • Date: December 2020
  • Journal: Communications of the ACM
  • Pages (from): 1
  • Pages (to): 14
  • Volume: x

Abstract

Graphs are by nature ‘unifying abstractions’ that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed? We are witnessing an unprecedented growth of interconnected data, which underscores the vital role of graph processing in our society. To name only a few remarkable examples of late, the importance of this field for practitioners is evidenced by the large number (over 50,000) of people registered2 to download the Neo4j book “​Graph Algorithms​” in just over 1.5 years, and by the enormous interest in the use of graph processing in the Artificial Intelligence and Machine Learning fields3. Furthermore, the timely Graphs4Covid-19 initiative4 provides evidence for the importance of big graph analytics in alleviating the global COVID-19 pandemic. This article addresses the questions: How do the next-decade big graph processing systems look like from the perspectives of the data management and the large scale systems communities5? What can we say today about the guiding design principles of these systems in the next 10 years?

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BibTeX

@article{sakr_sherif-2020-cacm,
  title =      "The Future is Big Graphs! A Community View on Graph
               Processing Systems",
  author =     "Sherif Sakr and Angela Bonifati and Hannes Voigt and
               Alexandru Iosup and Hsiang-Yun Wu and others",
  year =       "2020",
  abstract =   "Graphs are by nature ‘unifying abstractions’ that can
               leverage interconnectedness to represent, explore, predict,
               and explain real- and digital-world phenomena. Although real
               users and consumers of graph instances and graph workloads
               understand these abstractions, future problems will require
               new abstractions and systems. What needs to happen in the
               next decade for big graph processing to continue to succeed?
               We are witnessing an unprecedented growth of interconnected
               data, which underscores the vital role of graph processing
               in our society. To name only a few remarkable examples of
               late, the importance of this field for practitioners is
               evidenced by the large number (over 50,000) of people
               registered2 to download the Neo4j book “​Graph
               Algorithms​” in just over 1.5 years, and by the enormous
               interest in the use of graph processing in the Artificial
               Intelligence and Machine Learning fields3. Furthermore, the
               timely Graphs4Covid-19 initiative4 provides evidence for the
               importance of big graph analytics in alleviating the global
               COVID-19 pandemic. This article addresses the questions: How
               do the next-decade big graph processing systems look like
               from the perspectives of the data management and the large
               scale systems communities5? What can we say today about the
               guiding design principles of these systems in the next 10
               years?",
  month =      dec,
  journal =    "Communications of the ACM ",
  volume =     "x",
  pages =      "1--14",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/sakr_sherif-2020-cacm/",
}