Many Big Data applications in business and science require the management and analysis of huge amounts of graph data. Previous approaches for graph analytics such as graph databases and parallel graph processing systems (e.g., Pregel) either lack sufficient scalability or flexibility and expressiveness. We are therefore developing a new end-to-end approach for graph data management and analysis based on the Hadoop ecosystem, called Gradoop (Graph analytics on Hadoop). Gradoop is designed around the so-called Extended Property Graph Data Model (EPGM) supporting semantically rich, schema-free graph data within many distinct graphs. A set of high-level operators is provided for analyzing both single graphs and collections of graphs. Based on these operators, we propose a domain-specific language to define analytical workflows. The Gradoop graph store is currently utilizing HBase for distributed storage of graph data in Hadoop clusters. An initial version of Gradoop has been used to analyze graph data for business intelligence and social network analysis.
@article{arxiv.1506.00548,
title = {GRADOOP: Scalable Graph Data Management and Analytics with Hadoop},
author = {Martin Junghanns and André Petermann and Kevin Gómez and Erhard Rahm},
journal= {arXiv preprint arXiv:1506.00548},
year = {2015}
}