A distributed graph engine for web scale RDF data

  • Authors:

  • Kai Zeng;Jiacheng Yang;Haixun Wang;Bin Shao;Zhongyuan Wang

  • Affiliations:

  • UCLA;Columbia University;Microsoft Research Asia;Microsoft Research Asia;Microsoft Research Asia and Renmin University of China

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2013

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Abstract

Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively. Furthermore, many useful and general purpose graph-based operations (e.g., random walk, reachability, community discovery) on RDF data are not supported, as most existing systems store and index data in particular ways (e.g., as relational tables or as a bitmap matrix) to maximize one particular operation on RDF data: SPARQL query processing. In this paper, we introduce Trinity. RDF, a distributed, memory-based graph engine for web scale RDF data. Instead of managing the RDF data in triple stores or as bitmap matrices, we store RDF data in its native graph form. It achieves much better (sometimes orders of magnitude better) performance for SPARQL queries than the state-of-the-art approaches. Furthermore, since the data is stored in its native graph form, the system can support other operations (e.g., random walks, reachability) on RDF graphs as well. We conduct comprehensive experimental studies on real life, web scale RDF data to demonstrate the effectiveness of our approach.