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Distributing the power of OLAP

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dc.contributor.author Doka, K en
dc.contributor.author Tsoumakos, D en
dc.contributor.author Koziris, N en
dc.date.accessioned 2014-03-01T02:46:46Z
dc.date.available 2014-03-01T02:46:46Z
dc.date.issued 2010 en
dc.identifier.uri http://hdl.handle.net/123456789/32832
dc.subject Data cube en
dc.subject Data earehousing en
dc.subject P2P en
dc.subject.other Aggregate queries en
dc.subject.other Brown dwarfs en
dc.subject.other Centralized algorithms en
dc.subject.other Data cube en
dc.subject.other Data earehousing en
dc.subject.other Distributed systems en
dc.subject.other Multidimensional data en
dc.subject.other Network node en
dc.subject.other Overlay nodes en
dc.subject.other P2P en
dc.subject.other Storage overhead en
dc.subject.other Unstructured peer-to-peer en
dc.subject.other Geometry en
dc.subject.other Peer to peer networks en
dc.subject.other Stars en
dc.subject.other Systems analysis en
dc.subject.other Distributed computer systems en
dc.title Distributing the power of OLAP en
heal.type conferenceItem en
heal.identifier.primary 10.1145/1851476.1851521 en
heal.identifier.secondary http://dx.doi.org/10.1145/1851476.1851521 en
heal.publicationDate 2010 en
heal.abstract In this paper we present the Brown Dwarf, a distributed system designed to efficiently store, query and update multidimensional data over an unstructured Peer-to-Peer overlay, without the use of any proprietary tool. Brown Dwarf manages to distribute a highly effective centralized structure among peers on-the-y. Both point and aggregate queries are then naturally answered on-line through cooperating nodes that hold parts of a fully or partially materialized data cube. Updates are also performed on-line, eliminating the usually costly over-night process. Our initial evaluation on an actual testbed proves that Brown Dwarf manages to distribute the structure across the overlay nodes incurring only a small storage overhead compared to the centralized algorithm. Moreover, it accelerates cube creation up to 5 times and querying up to several tens of times by exploiting the capabilities of the available network nodes working in parallel. Copyright 2010 ACM. en
heal.journalName HPDC 2010 - Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing en
dc.identifier.doi 10.1145/1851476.1851521 en
dc.identifier.spage 324 en
dc.identifier.epage 327 en


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