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Declarative and distributed graph analytics with GRADOOP

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  • Declarative and distributed graph analytics with GRADOOP

Junghanns, M. ; Kießling, M. ; Teichmann, N. ; Gomez, K. ; Petermann, A. ; Rahm, E.

Declarative and distributed graph analytics with GRADOOP

PVLDB

2018 / 08

Paper

Futher information: http://www.vldb.org/pvldb/vol11/p2006-junghanns.pdf

Abstract

We demonstrate Gradoop, an open source framework that combines and extends features of graph database systems with the benefits of distributed graph processing. Using a rich graph data model and powerful graph operators, users can declaratively express graph analytical programs for distributed execution without needing advanced programming experience or a deeper understanding of the underlying sys-tem. Visitors of the demo can declare graph analytical pro-grams using the Gradoop operators and also visually expe-rience two of our advanced operators: graph pattern matching and graph grouping. We provide real world and artificial social network data with up to 10 billion edges and allow running the programs either locally or on a remote research cluster to demonstrate scalability.

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