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While work in recent years has demonstrated that wavelets can be efficiently used to compress large quantities of data and provide fast and fairly accurate answers to queries, little emphasis has been placed on using wavelets in approximating datasets containing multiple measures. Existing decomposition approaches will either operate on each measure individually, or treat all measures as a vector of values and process them simultaneously. We show in this paper that the resulting individual or combined storage approaches for the wavelet coefficients of different measures that stem from these existing algorithms may lead to suboptimal storage utilization, which results to reduced accuracy to queries. To alleviate this problem, we introduce in this work the notion of an extended wavelet coefficient as a flexible storage method for the wavelet coefficients, and propose novel algorithms for selecting which extended wavelet coefficients to retain under a given storage constraint. Experimental results with both real and synthetic datasets demonstrate that our approach achieves improved accuracy to queries when compared to existing techniques.
@inproceedings {DBLP:conf/sigmod/DeligiannakisR03, author = {Antonios Deligiannakis and Nick Roussopoulos}, booktitle = {SIGMOD Conference}, title = {Extended Wavelets for Multiple Measures.}, pages = {229-240}, year = {2003}, url = {db/conf/sigmod/sigmod2003.html#DeligiannakisR03}, ee = {http://www.acm.org/sigmod/sigmod03/eproceedings/papers/r09p01.pdf}, crossref = {conf/sigmod/2003}, bibsource = {DBLP, http://dblp.uni-trier.de} }  ©2004 Association for Computing Machinery |
