Big Graph Data Analytics on Single Machines – An Overview
| dc.contributor.author | Paradies, Marcus | |
| dc.contributor.author | Voigt, Hannes | |
| dc.date.accessioned | 2018-01-08T08:07:53Z | |
| dc.date.available | 2018-01-08T08:07:53Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Driven by a multitude of use cases, graph data analytics has become a hot topic in research and industry. Particularly on big graphs, performing complex analytical queries efficiently to derive new insights is a challenging task. Systems that aim at solving the technical part of this challenge are often referred to as graph processing systems. They allow expressing and executing analytic algorithms and queries, while hiding most of the technical details related to efficiently storing and processing graph data. Since 2010, work on graph processing systems for distributed systems as well as shared memory systems has virtually exploded. In this article, we give an overview of this work with the particular focus on graph processing systems for large multiprocessor machines. We describe the state of the art established in recent years and outline trends and challenges in research and development that point towards the future of graph processing systems. | |
| dc.identifier.pissn | 1610-1995 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11009 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Datenbank-Spektrum: Vol. 17, No. 2 | |
| dc.relation.ispartofseries | Datenbank-Spektrum | |
| dc.title | Big Graph Data Analytics on Single Machines – An Overview | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 112 | |
| gi.citation.startPage | 101 |