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Distributed Grouping of Property Graphs with GRADOOP

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dc.contributor.authorJunghanns, Martin
dc.contributor.authorPetermann, André
dc.contributor.authorRahm, Erhard
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorNicklas, Daniela
dc.contributor.editorLeymann, Frank
dc.contributor.editorSchöning, Harald
dc.contributor.editorHerschel, Melanie
dc.contributor.editorTeubner, Jens
dc.contributor.editorHärder, Theo
dc.contributor.editorKopp, Oliver
dc.contributor.editorWieland, Matthias
dc.date.accessioned2017-06-20T20:24:55Z
dc.date.available2017-06-20T20:24:55Z
dc.date.issued2017
dc.description.abstractProperty graphs are an intuitive way to model, analyze and visualize complex relationships among heterogeneous data objects, for example, as they occur in social, biological and information networks. These graphs typically contain thousands or millions of vertices and edges and their entire representation can easily overwhelm an analyst. One way to reduce complexity is the grouping of vertices and edges to summary graphs. In this paper, we present an algorithm for graph grouping with support for attribute aggregation and structural summarization by user-defined vertex and edge properties. The algorithm is part of G , an open-source system for graph analytics. G is implemented on top of Apache Flink, a state-of-the-art distributed dataflow framework, and thus allows us to scale graph analytical programs across multiple machines. Our evaluation demonstrates the scalability of the algorithm on real-world and synthetic social network data.en
dc.identifier.isbn978-3-88579-659-6
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2017)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-265
dc.subjectGraph Analytics
dc.subjectGraph Algorithms
dc.subjectDistributed Computing
dc.subjectDataflow systems
dc.titleDistributed Grouping of Property Graphs with GRADOOPen
dc.typeText/Conference Paper
gi.citation.endPage122
gi.citation.startPage103
gi.conference.date6.-10. März 2017
gi.conference.locationStuttgart
gi.conference.sessiontitleBig Data and NoSQL

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