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Temporal Graph Analysis using Gradoop

dc.contributor.authorRost, Christopher
dc.contributor.authorThor, Andreas
dc.contributor.authorRahm, Erhard
dc.contributor.editorMeyer, Holger
dc.contributor.editorRitter, Norbert
dc.contributor.editorThor, Andreas
dc.contributor.editorNicklas, Daniela
dc.contributor.editorHeuer, Andreas
dc.contributor.editorKlettke, Meike
dc.date.accessioned2019-04-15T11:40:29Z
dc.date.available2019-04-15T11:40:29Z
dc.date.issued2019
dc.description.abstractThe temporal analysis of evolving graphs is an important requirement in many domains but hardly supported in current graph database and graph processing systems. We therefore have started with extending Gradoop for temporal graph analysis by adding time properties to vertices, edges and graphs and using them within graph operators. We outline these extensions and show their use in a bibliographic scenario to analyze temporal citation patterns.en
dc.identifier.doi10.18420/btw2019-ws-11
dc.identifier.isbn978-3-88579-684-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/21797
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2019 – Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) – Proceedings, Volume P-290
dc.subjectTemporal Graph
dc.subjectTemporal Graph Data Model
dc.subjectGraph Analysis
dc.titleTemporal Graph Analysis using Gradoopen
gi.citation.endPage118
gi.citation.startPage109
gi.conference.date4.-8. März 2019
gi.conference.locationRostock
gi.conference.sessiontitleWorkshop on Big (and Small) Data in Science and Humanities (BigDS 2019)

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