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Extended wavelets for multiple measures

Deligiannakis Antonios, Garofalakis Minos, Roussopoulos Nick

Πλήρης Εγγραφή

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Περίληψη

While work in recent years has demonstrated that wavelets can be efficiently used to compresslarge quantities of data and provide fast and fairly accurate answers to queries, little emphasishas been placed on using wavelets in approximating datasets containing multiple measures.Existing decomposition approaches will either operate on each measure individually, or treat allmeasures as a vector of values and process them simultaneously. We show in this paper thatthe resulting individual or combined storage approaches for the wavelet coefficients of differentmeasures 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 thiswork the notion of an extended wavelet coefficient as a flexible storage method for the waveletcoefficients, and propose novel algorithms for selecting which extended wavelet coefficients toretain under a given storage constraint. Experimental results with both real and syntheticdatasets demonstrate that our approach achieves improved accuracy to queries when comparedto existing techniques.

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