The Big Picture: Understanding large-scale graphs using Graph Grouping with GRADOOP
dc.contributor.author | Junghanns, Martin | |
dc.contributor.author | Petermann, André | |
dc.contributor.author | Teichmann, Niklas | |
dc.contributor.author | Rahm, Erhard | |
dc.contributor.editor | Mitschang, Bernhard | |
dc.contributor.editor | Nicklas, Daniela | |
dc.contributor.editor | Leymann, Frank | |
dc.contributor.editor | Schöning, Harald | |
dc.contributor.editor | Herschel, Melanie | |
dc.contributor.editor | Teubner, Jens | |
dc.contributor.editor | Härder, Theo | |
dc.contributor.editor | Kopp, Oliver | |
dc.contributor.editor | Wieland, Matthias | |
dc.date.accessioned | 2017-06-20T20:24:51Z | |
dc.date.available | 2017-06-20T20:24:51Z | |
dc.date.issued | 2017 | |
dc.description.abstract | Graph grouping supports data analysts in decision making based on the characteristics of large-scale, heterogeneous networks containing millions or even billions of vertices and edges. We demonstrate graph grouping with G , a scalable system supporting declarative programs composed from multiple graph operations. Using social network data, we highlight the analytical capabilities enabled by graph grouping in combination with other graph operators. The resulting graphs are visualized and visitors are invited to either modify existing or write new analytical programs. 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. In the demonstration, programs can either be executed locally or remotely on our research cluster. | en |
dc.identifier.isbn | 978-3-88579-659-6 | |
dc.identifier.pissn | 1617-5468 | |
dc.language.iso | en | |
dc.publisher | Gesellschaft für Informatik, Bonn | |
dc.relation.ispartof | Datenbanksysteme für Business, Technologie und Web (BTW 2017) | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-265 | |
dc.subject | Graph Analytics | |
dc.subject | Graph Algorithms | |
dc.subject | Distributed Computing | |
dc.subject | Dataflow systems | |
dc.title | The Big Picture: Understanding large-scale graphs using Graph Grouping with GRADOOP | en |
dc.type | Text/Conference Paper | |
gi.citation.endPage | 631 | |
gi.citation.startPage | 629 | |
gi.conference.date | 6.-10. März 2017 | |
gi.conference.location | Stuttgart | |
gi.conference.sessiontitle | Demo Program |
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