Extended wavelets for multiple measuresExtended wavelets for multiple measures
Peer-Reviewed Journal Publication
Δημοσίευση σε Περιοδικό με Κριτές
2015-11-302007enWhile 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.http://creativecommons.org/licenses/by/4.0/ACM Transactions on Database Systems322
Deligiannakis Antonios
Δεληγιαννακης Αντωνιος
Garofalakis Minos
Γαροφαλακης Μινως
Roussopoulos Nick
Association for Computing Machinery
Database systems