Scalable graph analytics with GRADOOP
                                                              [Abstract]
                                                            Erhard Rahm
                                                         University of Leipzig
                                                          Augustusplatz 10
                                                          Leipzig, Germany
                                              rahm@informatik.uni-leipzig.de

ABSTRACT                                                                   About the Author
Many Big Data applications in business and science require                 Erhard Rahm is full professor for databases at the com-
the management and analysis of huge amounts of graph                       puter science institute of the University of Leipzig. His
data. Previous approaches for graph analytics such as graph                current research focusses on Big Data and data integra-
databases and parallel graph processing systems (e.g., Pregel)             tion. He has authored several books and more than 200
either lack sufficient scalability or flexibility and expressive-          peer-reviewed journal and conference publications. His re-
ness. We are therefore developing a new end-to-end ap-                     search on data integration and schema matching has been
proach for graph data management and analysis at the Big                   awarded several times, in particular with the renowned 10-
Data center of excellence ScaDS Dresden/Leipzig. The sys-                  year best-paper award of the conference series VLDB (Very
tem is called Gradoop (Graph analytics on Hadoop). Gradoop                 Large Databases) and the Influential Paper Award of the
is designed around the so-called Extended Property Graph                   conference series ICDE (Int. conf. on Data Engineering).
Data Model (EPGM) which supports semantically rich, schema-                Prof. Rahm is one of the two scientific coordinators of the
free graph data within many distinct graphs. A set of high-                new German center of excellence on Big Data ScaDS (com-
level operators is provided for analyzing both single graphs               petence center for SCAlable Data services and Solutions)
and sets of graphs. The operators are usable within a domain-              Dresden/Leipzig that started its operation in Oct. 2014.
specific language to define and run data integration work-
flows (for integrating heterogeneous source data into the
Gradoop graph store) as well as analysis workflows. The
Gradoop data store is currently utilizing HBase for distributed
storage of graph data in Hadoop clusters. An initial version
of Gradoop is operational and has been used for analyzing
graph data for business intelligence and social network anal-
ysis.




27th GI-Workshop on Foundations of Databases (Grundlagen von Daten-
banken), 26.05.2015 - 29.05.2015, Magdeburg, Germany.
Copyright is held by the author/owner(s).

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