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Conference Paper

Database foundations for scalable RDF processing

MPS-Authors
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;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45609

Theobald,  Martin
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Hose, K., Schenkel, R., Theobald, M., & Weikum, G. (2011). Database foundations for scalable RDF processing. In A. Polleres, C. d'Amato, M. Arenas, S. Handschuh, P. Kroner, S. Ossowski, et al. (Eds.), Reasoning Web (pp. 202-249). Berlin: Springer. doi:10.1007/978-3-642-23032-5_4.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-1439-A
Abstract
As more and more data is provided in RDF format, storing huge amounts of RDF data and efficiently processing queries on such data is becoming increasingly important. The first part of the lecture will introduce state-of-the-art techniques for scalably storing and querying RDF with relational systems, including alternatives for storing RDF, efficient index structures, and query optimization techniques. As centralized RDF repositories have limitations in scalability and failure tolerance, decentralized architectures have been proposed. The second part of the lecture will highlight system architectures and strategies for distributed RDF processing. We cover search engines as well as federated query processing, highlight differences to classic federated database systems, and discuss efficient techniques for distributed query processing in general and for RDF data in particular. Extracting knowledge from the Web is an excellent showcase -- and potentially one of the biggest challenges -- for the scalable management of uncertain data we have seen so far. The third part of the lecture is intended to provide a close-up on current approaches and platforms to make reasoning (e.g., in the form of probabilistic inference) with uncertain RDF data scalable to billions of triples.