http://www.cnr.it/ontology/cnr/individuo/prodotto/ID184106
Improving Range-Sum Query Evaluation on Data Cubes via Polynomial Approximation (Articolo in rivista)
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- Label
- Improving Range-Sum Query Evaluation on Data Cubes via Polynomial Approximation (Articolo in rivista) (literal)
- Anno
- 2006-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.datak.2005.03.011 (literal)
- Alternative label
Alfredo Cuzzocrea (2006)
Improving Range-Sum Query Evaluation on Data Cubes via Polynomial Approximation
in Data & knowledge engineering (Online); ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, AMSTERDAM (Paesi Bassi)
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- Alfredo Cuzzocrea (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Titolo
- Improving Range-Sum Query Evaluation on Data Cubes via Polynomial Approximation (literal)
- Abstract
- Inefficient query answering is the main drawback in Decision Support Systems (DSS), due to the very
large size of the multidimensional data stored in the underlying Data Warehouse Server (DWS). Aggregate
queries are the most frequent and useful kind for such systems, as they support several analysis based on the
multidimensionality and multi-resolution of data. As a consequence, providing fast answers to aggregate
queries (by trading off accuracy for efficiency, if possible) has become a very important requirement in
improving the effectiveness of DSS-based applications. In this paper we present a technique based on an
analytical interpretation of multidimensional data and on the well-known least squares approximation
(LSA) method for supporting approximate aggregate query answering in OLAP, which represents the most
common application interfaces for a DWS. Our technique consists in building data synopses by interpreting
the original data distributions as a set of discrete functions. These synopses, called D-Syn, are obtained by
approximating data with a set of polynomial coefficients, and by storing these coefficients instead of the
original data. Queries are issued on the compressed representation, thus reducing the number of disk accesses
needed to evaluate the answers. (literal)
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