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 handling and analyzing such graph data is still weak and scalability to large data volumes is often limited. We introduce the BIGGR approach provid...

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Bibliographic Details
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
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Summary: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.
ISSN:1618-2162
1610-1995
DOI:10.1007/s13222-019-00306-x