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dc.contributor.authorPears, R
dc.date.accessioned2013-02-26T04:48:42Z
dc.date.available2013-02-26T04:48:42Z
dc.date.copyright2009
dc.date.issued2013-02-26
dc.identifier.citationIn: Advanced Principles for Improving Database Design, System Modelling, and Software Developmentedited by Siau, K; Erickson, J
dc.identifier.urihttp://hdl.handle.net/10292/5199
dc.description.abstractData Warehouses are widely used for supporting decision making. On Line Analytical Processing or OLAP is the main vehicle for querying data warehouses. OLAP operations commonly involve the computation of multidimensional aggregates. The major bottleneck in computing these aggregates is the large volume of data that needs to be processed which in turn leads to prohibitively expensive query execution times. On the other hand, Data Analysts are primarily concerned with discerning trends in the data and thus a system that provides approximate answers in a timely fashion would suit their requirements better. In this chapter we present the Prime Factor scheme, a novel method for compressing data in a warehouse. Our data compression method is based on aggregating data on each dimension of the data warehouse. Extensive experimentation on both real-world and synthetic data have shown that it outperforms the Haar Wavelet scheme with respect to both decoding time and error rate, while maintaining comparable compression ratios (Pears and Houliston, 2007). One encouraging feature is the stability of the error rate when compared to the Haar Wavelet. Although Wavelets have been shown to be effective at compressing data, the approximate answers they provide varies widely, even for identical types of queries on nearly identical values in distinct parts of the data. This problem has been attributed to the thresholding technique used to reduce the size of the encoded data and is an integral part of the Wavelet compression scheme. In contrast the Prime Factor scheme does not rely on thresholding but keeps a smaller version of every data element from the original data and is thus able to achieve a much higher degree of error stability which is important from a Data Analysts point of view.
dc.publisherIGI Global Publishing
dc.rightsCopying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
dc.subjectData warehousing
dc.subjectOLAP
dc.subjectMulti dimensional aggregates
dc.subjectApproximate query processing
dc.subjectWavelets
dc.subjectPrime numbers
dc.subjectData compression
dc.titleAccelerating multi dimensional queries in data warehouses
dc.typeChapter in Book
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.4018/978-1-60566-172-8.ch011
dc.identifier.roid7845en_NZ
aut.publication.placeUSA
aut.relation.endpage203
aut.relation.startpage178
pubs.elements-id1596


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