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Efficient updates for a shared nothing analytics platform

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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/123456789/32839
dc.subject data cube en
dc.subject data warehousing en
dc.subject distributed systems en
dc.subject time series en
dc.subject updates en
dc.subject.other Customizable en
dc.subject.other Data cube en
dc.subject.other Data warehousing en
dc.subject.other Distributed storage en
dc.subject.other Distributed systems en
dc.subject.other Ordered data en
dc.subject.other Prototype implementations en
dc.subject.other Query response en
dc.subject.other Shared nothing en
dc.subject.other Target application en
dc.subject.other Time-series data en
dc.subject.other Software prototyping en
dc.subject.other Time series en
dc.subject.other Warehouses en
dc.subject.other World Wide Web en
dc.subject.other Data warehouses en
dc.title Efficient updates for a shared nothing analytics platform en
heal.type conferenceItem en
heal.identifier.primary 10.1145/1779599.1779606 en
heal.identifier.secondary 1779606 en
heal.identifier.secondary http://dx.doi.org/10.1145/1779599.1779606 en
heal.publicationDate 2010 en
heal.abstract In this paper we describe a cloud-based data-warehouselike system especially targeted to time series data. Apart from the benefits that a distributed storage built on top of a shared-nothing architecture offers, our system is designed to efficiently cope with continuous, on-line updates of temporally ordered data without compromising the query throughput. Through a totally customizable process performing asynchronous aggregation of past records, we achieve significant gains in storage and update times compared to traditional methods, maintaining a high accuracy in query responses for our target application. Experiments using our prototype implementation over an actual testbed prove that our scheme considerably accelerates (by a factor above 3) the update procedure and reduces required storage by at least 30%. We also show how these gains are related to the level and rate of aggregation performed. © 2010 ACM. en
heal.journalName ACM International Conference Proceeding Series en
dc.identifier.doi 10.1145/1779599.1779606 en


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