BIGGR: Bringing Gradoop to Applications

• Published in: Datenbank-Spektrum : Zeitschrift für Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft für Informatik e.V 2019-03, Vol.19 (1), p.51-60
• Main Authors: Rostami, M. Ali, Kricke, Matthias, Peukert, Eric, Kühne, Stefan, Wilke, Moritz, Dienst, Steffen, Rahm, Erhard
• Format: Article
• Language: English
• Subjects: Analytics; Bioinformatics; Citation analysis; Computer Science; Computer Systems Organization and Communication Networks; Data Mining and Knowledge Discovery; Data reduction; Data Structures and Information Theory; Database Management; Distributed processing; Graphs; Information Storage and Retrieval; IT in Business; Open source software; Schwerpunktbeitrag; Social networks; Source code; Visualization
• ISSN: 1618-2162; 1610-1995
• DOI: 10.1007/s13222-019-00306-x

Analyzing large amounts of graph data, e.g., from social networks or bioinformatics, has recently gained much attention. Unfortunately, tool support for handling 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 visualization of Big Graph Data on top of the open-source software KNIME and gradoop . Users can visually program graph analytics workflows, execute them on top of the distributed processing framework Apache Flink and visualize large graphs within KNIME. For visualization, 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 graphs.