H2RDF: Adaptive query processing on RDF data in the cloud

Papailiou, N; Konstantinou, I; Tsoumakos, D; Koziris, N

dc.contributor.author	Papailiou, N	en
dc.contributor.author	Konstantinou, I	en
dc.contributor.author	Tsoumakos, D	en
dc.contributor.author	Koziris, N	en
dc.date.accessioned	2014-03-01T02:53:39Z
dc.date.available	2014-03-01T02:53:39Z
dc.date.issued	2012	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36469
dc.subject	Hadoop	en
dc.subject	HBase	en
dc.subject	MapReduce	en
dc.subject	NoSQL	en
dc.subject	RDF	en
dc.subject	SparQL	en
dc.subject.other	Hadoop	en
dc.subject.other	HBase	en
dc.subject.other	Map-reduce	en
dc.subject.other	NoSQL	en
dc.subject.other	RDF	en
dc.subject.other	SparQL	en
dc.subject.other	Algorithms	en
dc.subject.other	Queueing networks	en
dc.subject.other	World Wide Web	en
dc.title	H2RDF: Adaptive query processing on RDF data in the cloud	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/2187980.2188058	en
heal.identifier.secondary	http://dx.doi.org/10.1145/2187980.2188058	en
heal.publicationDate	2012	en
heal.abstract	In this work we present H2RDF, a fully distributed RDF store that combines the MapReduce processing framework with a NoSQL distributed data store. Our system features two unique characteristics that enable efficient processing of both simple and multi-join SPARQL queries on virtually unlimited number of triples: Join algorithms that execute joins according to query selectivity to reduce processing; and adaptive choice among centralized and distributed (MapReduce-based) join execution for fast query responses. Our system efficiently answers both simple joins and complex multivariate queries and easily scales to 3 billion triples using a small cluster of 9 worker nodes. H2RDF outperforms state-of-the-art distributed solutions in multi-join and nonselective queries while achieving comparable performance to centralized solutions in selective queries. In this demonstration we showcase the system's functionality through an interactive GUI. Users will be able to execute predefined or custom-made SPARQL queries on datasets of different sizes, using different join algorithms. Moreover, they can repeat all queries utilizing a different number of cluster resources. Using real-time cluster monitoring and detailed statistics, participants will be able to understand the advantages of different execution schemes versus the input data as well as the scalability properties of H2RDF over both the data size and the available worker resources. Copyright is held by the International World Wide Web Conference Committee (IW3C2).	en
heal.journalName	WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion	en
dc.identifier.doi	10.1145/2187980.2188058	en
dc.identifier.spage	397	en
dc.identifier.epage	400	en