Information
- Publication Type: Journal Paper (without talk)
- Workgroup(s)/Project(s): not specified
- Date: December 2020
- Journal: Communications of the ACM
- Volume: x
- Pages: 1 – 14
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?Additional Files and Images
Weblinks
No further information available.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/", }