توضیحات
ABSTRACT
The generation of RDF data has accelerated to the point where many data sets need to be partitioned across mul-tiple machines in order to achieve reasonable performance when querying the data. Although tremendous progress has been made in the Semantic Web community for achieving high performance data management on a single node, cur- rent solutions that allow the data to be partitioned across multiple machines are highly inecient. In this paper, we introduce a scalable RDF data management system that is up to three orders of magnitude more ecient than popular multi-node RDF data management systems. In so doing, we introduce techniques for (1) leveraging state-of-the-art single node RDF-store technology (2) partitioning the data across nodes in a manner that helps accelerate query pro- cessing through locality optimizations and (3) decomposing SPARQL queries into high performance fragments that take advantage of how data is partitioned in a cluster
INTRODUCTION
The proliferation of RDF (Resource Description Frame- work [2]) data is accelerating. The reasons for this include: major search engines such as Google (RichSnippets) and Ya-hoo (SearchMonkey) displaying Webpages marked up with RDFa more prominently in search results (thereby encourag-ing increased used of RDFa), popular content management systems such as Drupal o ering increased support for RDF, and more data sets becoming available to the public (espe- cially from government sources) in RDF format. Although the increased amount of RDF available is good for Semantic Web initiatives, it is also causing performance bottlenecks in currently available RDF data management systems that store the data and provide access to it via query interfaces such as SPARQL
year : 2011
Publisher : VLDB
By : Jiewen Huang , Daniel J. Abadi ,Kun Ren
File Information : English Language / 12 Page / Size : 413 K
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سال : 2011
ناشر : VLDB
کاری از : Jiewen Huang , Daniel J. Abadi ,Kun Ren
اطلاعات فایل : زبان انگلیسی / 12 صفحه / حجم : 413 K
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