German English

GRADOOP: Scalable Graph Data Management and Analytics with Hadoop

Processing highly connected data as graphs becomes more and more important in many different domains. Prominent examples are social networks, e.g. facebook and Twitter, as well as information networks like the World Wide Web or biological networks. One important similarity of these domain specific data is their inherent graph structure which makes them eligible for analytics using graph algorithms. Besides that, the datasets share two more similarities: they are huge in size, making it hard or even impossible to process them on a single machine and they grow over time, which classifies them as dynamic graphs. With the objective of analyzing these large-scale, dynamic datasets, we started developing a framework called “Gradoop” (Graph Analytics on Hadoop) with the following three main objectives:

  1. developing a graph data model incl. operators for the definition of analytical pipelines
  2. data integration of heterogeneous source systems into an integrated graph and
  3. efficient data distribution / replication to optimize the execution of distributed graph operators.

Our prototype is build on top of Apache Flink and Apache HBase. The data model has been designed and a set of sample operators have been implemented. A first use case is the BIIIG project for graph analytics in business information networks. In our ongoing work, we will look into different methods of graph partitioning to accelerate the execution of analytical pipelines.

People

Research

Students

  • Kevin Gomez
  • Niklas Teichmann
  • Stephan Kemper

Source code

GitHub

Cooperation


Competence Center for Scalable Data Services and Solutions (ScaDS)

Talks

Publications



Google Scholar
publication iconPetermann, A.; Junghanns, M.
Scalable Business Intelligence with Graph Collections
it - Information Technology Special Issue: Big Data Analytics
2016-08
PDF

Google Scholar
publication iconJunghanns, M.; Petermann, A.; Teichmann, N.; Gomez, K.; Rahm, E.
Analyzing Extended Property Graphs with Apache Flink
Proc. Int. SIGMOD workshop on Network Data Analytics (NDA)
2016-07
PDF

Google Scholar
publication iconJunghanns, M.; Petermann, A.; Gomez, K.; Rahm, E.
GRADOOP: Scalable Graph Data Management and Analytics with Hadoop
Techn. Report, Univ. of Leipzig, June 2015
2015-06
PDF

Google Scholar
publication iconRahm, Erhard
Scalable graph analytics with GRADOOP
Proc. GI-Workshop Grundlagen von Datenbanksystemen (GvDB), Gommern, May 2015 (Invited Talk)
2015-05
PDF

Google Scholar
Petermann, A.; Junghanns, M.; Müller, R.; Rahm, E.
Graph-based Data Integration and Business Intelligence with BIIIG
Proc. VLDB Conf., 2014 (Demo paper)
2014-09
PDF

Google Scholar
publication iconPetermann, A.; Junghanns, M.; Müller, R.; Rahm, E.
FoodBroker - Generating Synthetic Datasets for Graph-Based Business Analytics
5th Workshop on Big Data Benchmarking (WBDB 2014), LNCS 8991, 2015
2014-08
PDF

Google Scholar
publication iconPetermann, A.; Junghanns, M.; Müller, R.; Rahm, E.
BIIIG : Enabling Business Intelligence with Integrated Instance Graphs
5th International Workshop on Graph Data Management (GDM 2014)
2014-03