Paper Info
Title
Big Graph Data Analytics on Single Machines - An Overview.
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.
Year
Venue
Field
2017
Datenbank-Spektrum
Data science,Data mining,Graph,Graph database,Use case,Data analysis,Shared memory,Computer science,Multiprocessing,Theoretical computer science,Wait-for graph,Database
DocType
Volume
Issue
Journal
17
2
Citations 
PageRank 
References 
0
0.34
78
Authors
2
Name
Order
Citations
PageRank
Marcus Paradies18210.36
Hannes Voigt26819.45