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BIGGR: Bringing Gradoop to Applications


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publication iconRostami, M.A.; Kricke, M.; Peukert, E.; Kühne, S.; Wilke, M.; Dienst, S.; Rahm, E.
BIGGR: Bringing Gradoop to Applications


Analyzing large amounts of graph data, e.g. from social networks or bioinformatics, has recently gained much attention. Unfortunately, tool support for han-dling and analyzing such graph data is still weak and scalability to large data volumes is often limited. We introduce the BIGGR approach providing a novel tool for the user-friendly and efficient analysis and visualiza-tion of Big Graph Data on top of the open-source soft-ware KNIME and Gradoop. Users can visually pro-gram graph analytics workflows, execute them on top of the distributed processing framework Apache Flink and visualize large graphs within KNIME. For visu-alization we apply visualization-driven data reduction techniques by pushing down sampling and layouting to Gradoop and Apache Flink. We also discuss an initial application of the tool for the analysis of patent citation graps.