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Partout: A Distributed Engine for Efficient RDF Processing

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44469

Galárraga,  Luis
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44645

Hose,  Katja
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45380

Schenkel,  Ralf
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Galárraga, L., Hose, K., & Schenkel, R. (2012). Partout: A Distributed Engine for Efficient RDF Processing. arXiv, abs/1212.5636, 1-12. Retrieved from http://arxiv.org/abs/1212.5636.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-58D2-3
Abstract
The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications with already more than a trillion triples in some cases. Confronted with such huge amounts of data and the future growth, existing state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce Partout, a distributed engine for efficient RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log, allocating the fragments to nodes in a cluster, and finding the optimal configuration. Partout can efficiently handle updates and its query optimizer produces efficient query execution plans for ad-hoc SPARQL queries. Our experiments show the superiority of our approach to state-of-the-art approaches for partitioning and distributed SPARQL query processing.