Brown Dwarf: A fully-distributed, fault-tolerant data warehousing system

Doka, K; Tsoumakos, D; Koziris, N

dc.contributor.author	Doka, K	en
dc.contributor.author	Tsoumakos, D	en
dc.contributor.author	Koziris, N	en
dc.date.accessioned	2014-03-01T01:35:22Z
dc.date.available	2014-03-01T01:35:22Z
dc.date.issued	2011	en
dc.identifier.issn	0743-7315	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21021
dc.subject	Data cube	en
dc.subject	Data warehousing	en
dc.subject	Peer-to-Peer	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Analytical queries	en
dc.subject.other	Brown dwarfs	en
dc.subject.other	Data cube	en
dc.subject.other	Data warehousing systems	en
dc.subject.other	Distributed data	en
dc.subject.other	Distributed data structures	en
dc.subject.other	Fault-tolerant	en
dc.subject.other	Indexing structures	en
dc.subject.other	Multidimensional data	en
dc.subject.other	Network node	en
dc.subject.other	Node failure	en
dc.subject.other	On-the-fly	en
dc.subject.other	Parallelizations	en
dc.subject.other	Peer to peer	en
dc.subject.other	Skewed data	en
dc.subject.other	Unstructured peer-to-peer	en
dc.subject.other	Data structures	en
dc.subject.other	Data warehouses	en
dc.subject.other	Distributed computer systems	en
dc.subject.other	Geometry	en
dc.subject.other	Stars	en
dc.subject.other	Systems analysis	en
dc.subject.other	Warehouses	en
dc.subject.other	Search engines	en
dc.title	Brown Dwarf: A fully-distributed, fault-tolerant data warehousing system	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jpdc.2011.07.008	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jpdc.2011.07.008	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	In this paper we present the Brown Dwarf, a distributed data analytics system designed to efficiently store, query and update multidimensional data over commodity network nodes, without the use of any proprietary tool. Brown Dwarf distributes a centralized indexing structure among peers on-the-fly,. reducing cube creation and querying times by enforcing parallelization. Analytical queries are naturally performed on-line through cooperating nodes that form an unstructured Peer-to-Peer overlay. Updates are also performed on-line, eliminating the usually costly over-night process. Moreover, the system employs an adaptive replication scheme that adjusts to the workload skew as well as the network churn by expanding or shrinking the units of the distributed data structure. Our system has been thoroughly evaluated on an actual testbed: it manages to accelerate cube creation up and querying up to several tens of times compared to the centralized solution by exploiting the capabilities of the available network nodes working in parallel. It also manages to quickly adapt even after sudden bursts in load and remains unaffected with a considerable fraction of frequent node failures. These advantages are even more apparent for dense and skewed data cubes and workloads. (C) 2011 Elsevier Inc. All rights reserved.	en
heal.publisher	ACADEMIC PRESS INC ELSEVIER SCIENCE	en
heal.journalName	Journal of Parallel and Distributed Computing	en
dc.identifier.doi	10.1016/j.jpdc.2011.07.008	en
dc.identifier.isi	ISI:000295018500002	en
dc.identifier.volume	71	en
dc.identifier.issue	11	en
dc.identifier.spage	1434	en
dc.identifier.epage	1446	en