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Management and analysis of big graph data: Current systems and open challenges
Leipzig University, Germany.
Leipzig University, Germany.
RISE - Research Institutes of Sweden, ICT, SICS.
Leipzig University, Germany.
2017 (English)In: Handbook of Big Data Technologies, Springer , 2017, 457-505 p.Chapter in book (Other academic)
Abstract [en]

Many big data applications in business and science require the management and analysis of huge amounts of graph data. Suitable systems to manage and to analyze such graph data should meet a number of challenging requirements including support for an expressive graph data model with heterogeneous vertices and edges, powerful query and graph mining capabilities, ease of use as well as high performance and scalability. In this chapter, we survey current system approaches for management and analysis of "big graph data". We discuss graph database systems, distributed graph processing systems such as Google Pregel and its variations, and graph dataflow approaches based on Apache Spark and Flink. We further outline a recent research framework called Gradoop that is build on the so-called Extended Property Graph Data Model with dedicated support for analyzing not only single graphs but also collections of graphs. Finally, we discuss current and future research challenges.

Place, publisher, year, edition, pages
Springer , 2017. 457-505 p.
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-31028DOI: 10.1007/978-3-319-49340-4_14Scopus ID: 2-s2.0-85019929220ISBN: 9783319493404 ISBN: 9783319493398 OAI: oai:DiVA.org:ri-31028DiVA: diva2:1138600
Available from: 2017-09-06 Created: 2017-09-06 Last updated: 2017-09-06Bibliographically approved

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CiteExportLink to record
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  • apa
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