Scalable semantic web data management using vertical partitioning Export

@inproceedings{citeulike:2217125, abstract = {Efficient management of RDF data is an important factor in realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance for RDF databases and consider a recent suggestion, "property tables." We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertical partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds.}, author = {Abadi, Daniel J. and Marcus, Adam and Madden, Samuel R. and Hollenbach, Kate}, booktitle = {VLDB '07: Proceedings of the 33rd international conference on Very large data bases}, citeulike-article-id = {2217125}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1325851.1325900}, isbn = {978-1-59593-649-3}, keywords = {2007, column-store, partitioning, rdf, rdf\_databases, vertical-partitioning, vldb}, location = {Vienna, Austria}, pages = {411--422}, posted-at = {2009-10-06 10:48:25}, priority = {2}, publisher = {VLDB Endowment}, title = {Scalable semantic web data management using vertical partitioning}, url = {http://portal.acm.org/citation.cfm?id=1325851.1325900}, year = {2007} }

TY - CONF ID - citeulike:2217125 L3 - citeulike-article-id:2217125 N2 - Efficient management of RDF data is an important factor in realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance for RDF databases and consider a recent suggestion, "property tables." We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertical partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds. CY - Vienna, Austria SN - 978-1-59593-649-3 EP - 422 TI - Scalable semantic web data management using vertical partitioning PB - VLDB Endowment T2 - VLDB '07: Proceedings of the 33rd international conference on Very large data bases SP - 411 KW - 2007 KW - column-store KW - partitioning KW - rdf KW - rdf_databases KW - vertical-partitioning KW - vldb AU - Abadi, Daniel AU - Marcus, Adam AU - Madden, Samuel AU - Hollenbach, Kate PY - 2007/// UR - http://portal.acm.org/citation.cfm?id=1325851.1325900 ER -