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Graph Sampling with Distributed In-Memory Dataflow Systems

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dc.contributor.authorGomez, Kevin
dc.contributor.authorTäschner, Matthias
dc.contributor.authorRostami, M. Ali
dc.contributor.authorRost, Christopher
dc.contributor.authorRahm, Erhard
dc.contributor.editorKai-Uwe Sattler
dc.contributor.editorMelanie Herschel
dc.contributor.editorWolfgang Lehner
dc.date.accessioned2021-03-16T07:57:10Z
dc.date.available2021-03-16T07:57:10Z
dc.date.issued2021
dc.description.abstractGiven a large graph, graph sampling determines a subgraph with similar characteristics for certain metrics of the original graph. The samples are much smaller thereby accelerating and simplifying the analysis and visualization of large graphs. We focus on the implementation of distributed graph sampling for Big Data frameworks and in-memory dataflow systems such as Apache Spark or Apache Flink and evaluate the scalability of the new implementations. The presented methods will be open source and be integrated into Gradoop, a system for distributed graph analytics.en
dc.identifier.doi10.18420/btw2021-15
dc.identifier.isbn978-3-88579-705-0
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/35798
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2021
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-311
dc.subjectGraph Analytics
dc.subjectDistributed Computing
dc.subjectGraph Sampling
dc.subjectData Integration
dc.titleGraph Sampling with Distributed In-Memory Dataflow Systemsen
gi.citation.endPage312
gi.citation.startPage303
gi.conference.date13.-17. September 2021
gi.conference.locationDresden
gi.conference.sessiontitleData Integration, Semantic Data Management, Streaming

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