Abstract
We are witnessing the evolution of the Web from a worldwide information space of linked documents to a global knowledge base, composed of semantically interconnected resources (to date, 25 billion RDF triples, interlinked by around 395 million RDF links). RDF comes equipped with the SPARQL language for querying data in RDF format. Although many aspects of the challenges faced in large-scale RDF data management have already been studied in the database research community, current approaches provide centralized hard-coded solutions, with high consumption of resources; moreover, these exhibit very limited flexibility dealing with queries, at various levels of granularity and complexity (e.g. so-called non-conjunctive queries that use SPARQL’s union or optional). In this paper we propose a general model for answering SPARQL queries based on the first principles of linear algebra, in particular on tensorial calculus. Leveraging our abstract algebraic framework, our technique allows both quick decentralized processing, and centralized massive analysis. Experimental results show that our approach, utilizing recent linear algebra techniques—tailored to performance and accuracy as required in applied mathematics and physics fields—can process analysis efficiently, when compared to competitors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arias, M., Fernández, J.D., Martínez-Prieto, M.A., de la Fuente, P.: An empirical study of real-world sparql queries. CoRR abs/1103.5043 (2011)
Bondy, A., Murty, U.S.R.: Graph Theory. LNCS. Springer (2010)
Chong, E., Das, S., Eadon, G., Srinivasan, J.: An efficient SQL-based RDF querying scheme. In: VLDB, pp. 1216–1227. ACM (2005)
Abadi, D.J., Marcus, A., Madden, S.R., Hollenbach, K.: SW-Store: a vertically partitioned DBMS for semantic web data management. VLDB J. 18(2), 385–406 (2009)
Davis, T.A.: Direct Methods for Sparse Linear Systems. SIAM (2006)
De Virgilio, R., Milicchio, F.: RFID data analysis using tensor calculus for supply chain management. In: CIKM, pp. 1743–1748. ACM (2011)
Möller, K., Havsenbles, M., Cyganiak, R., Grimnes, G.: Learning from linked open data usage: Patterns & metrics. In: WebSci. Web Science Trust, pp. 1–8 (2010)
Neumann, T., Weikum, G.: The RDF-3X engine for scalable management of RDF data. VLDB J. 19(1), 91–113 (2010)
Neumann, T., Weikum, G.: Scalable join processing on very large rdf graphs. In: SIGMOD, pp. 627–640. ACM (2009)
Neumann, T., Weikum, G.: x-rdf-3x: Fast querying, high update rates, and consistency for rdf databases. PVLDB 3(1-2), 256–263 (2010)
Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. Trans. Database Syst. 34(3), 16:1–16:45 (2009)
Prud’hommeaux, E., Seaborne, A.: SPARQL query language for RDF (2008)
Schmidt, M., Meier, M., Lausen, G.: Foundations of SPARQL query optimization. In: ICDT, pp. 4–33. ACM (2010)
Vidal, M.E., Ruckhaus, E., Lampo, T., Martínez, A., Sierra, J., Polleres, A.: Efficiently Joining Group Patterns in SPARQL Queries. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 228–242. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
De Virgilio, R. (2012). A Linear Algebra Technique for (de)Centralized Processing of SPARQL Queries. In: Atzeni, P., Cheung, D., Ram, S. (eds) Conceptual Modeling. ER 2012. Lecture Notes in Computer Science, vol 7532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34002-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-34002-4_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34001-7
Online ISBN: 978-3-642-34002-4
eBook Packages: Computer ScienceComputer Science (R0)