OLAP*: Effectively and efficiently supporting parallel OLAP over big data

Cuzzocrea, A; Moussa, R; Xu, G

OLAP*: Effectively and efficiently supporting parallel OLAP over big data

Cuzzocrea, A Moussa, R Xu, G

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8216 LNCS pp. 38 - 49
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Cuzzocrea, A	en_US
dc.contributor.author	Moussa, R	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8216 LNCS pp. 38 - 49	en_US
dc.identifier.isbn	9783642413650	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27891
dc.identifier.uri	http://hdl.handle.net/10453/33751
dc.description.abstract	In this paper, we investigate solutions relying on data partitioning schemes for parallel building of OLAP data cubes, suitable to novel Big Data environments, and we propose the framework OLAP, along with the associated benchmark TPC-Hd, a suitable transformation of the well-known data warehouse benchmark TPC-H. We demonstrate through performance measurements the efficiency of the proposed framework, developed on top of the ROLAP server Mondrian. © Springer-Verlag 2013.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-41366-7_4	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	OLAP*: Effectively and efficiently supporting parallel OLAP over big data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8216 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	8216 LNCS	en_US

Abstract:

In this paper, we investigate solutions relying on data partitioning schemes for parallel building of OLAP data cubes, suitable to novel Big Data environments, and we propose the framework OLAP*, along with the associated benchmark TPC-H*d, a suitable transformation of the well-known data warehouse benchmark TPC-H. We demonstrate through performance measurements the efficiency of the proposed framework, developed on top of the ROLAP server Mondrian. © Springer-Verlag 2013.

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http://hdl.handle.net/10453/27891