Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment

Dariusz Rafal Augustyn; Lukasz Warchal

doi:10.1007/978-3-319-01863-8_24

Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment

Dariusz Rafal Augustyn
, Lukasz Warchal

Department of Applied Informatics (RAU/RAU7)

Silesian University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Query selectivity is a parameter that allows to estimate the size of data satisfying a query condition. For complex range query condition it may be defined as multi integral over a multivariate probability density function (PDF). It describes a multidimensional attribute value distribution and may be estimated using the known approach based on a superposition of Gaussian clusters. But there is the problem of an efficient integration of the multivariate PDF. This may be solved by applying Monte Carlo (MC) method which exposes its advantages for high dimensions. To satisfy the time constraint of selectivity calculation, the parallelized MC integration method was proposed in the paper. The implementation of the method is based on CUDA technology. The paper also describes the application designated for obtaining the time-optimal parameter values of the method.

Original language	English
Title of host publication	New Trends in Databases and Information Systems - Selected Papers of the 17th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events
Editors	Tania Cerquitelli, Silvia Chiusano, Athena Vakali, Boris Novikov, Jaroslav Pokorný, Barbara Catania, Giovanna Guerrini, Alfons Kemper, Themis Palpanas, Mirko Kämpf
Publisher	Springer Verlag
Pages	215-224
Number of pages	10
ISBN (Electronic)	9783319018621
DOIs	https://doi.org/10.1007/978-3-319-01863-8_24
Publication status	Published - 2014
Event	17th East European Conference on Advances in Databases and Information Systems, ADBIS 2013 - Genoa, Italy Duration: 1 Sept 2013 → 4 Sept 2013

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	241
ISSN (Print)	2194-5357

Conference

Conference	17th East European Conference on Advances in Databases and Information Systems, ADBIS 2013
Country/Territory	Italy
City	Genoa
Period	1/09/13 → 4/09/13

Keywords

CUDA
Data clustering
Monte carlo integration
Selectivity estimation

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-319-01863-8_24

Cite this

Augustyn, D. R., & Warchal, L. (2014). Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment. In T. Cerquitelli, S. Chiusano, A. Vakali, B. Novikov, J. Pokorný, B. Catania, G. Guerrini, A. Kemper, T. Palpanas, & M. Kämpf (Eds.), New Trends in Databases and Information Systems - Selected Papers of the 17th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events (pp. 215-224). (Advances in Intelligent Systems and Computing; Vol. 241). Springer Verlag. https://doi.org/10.1007/978-3-319-01863-8_24

Augustyn, Dariusz Rafal ; Warchal, Lukasz. / Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment. New Trends in Databases and Information Systems - Selected Papers of the 17th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events. editor / Tania Cerquitelli ; Silvia Chiusano ; Athena Vakali ; Boris Novikov ; Jaroslav Pokorný ; Barbara Catania ; Giovanna Guerrini ; Alfons Kemper ; Themis Palpanas ; Mirko Kämpf. Springer Verlag, 2014. pp. 215-224 (Advances in Intelligent Systems and Computing).

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abstract = "Query selectivity is a parameter that allows to estimate the size of data satisfying a query condition. For complex range query condition it may be defined as multi integral over a multivariate probability density function (PDF). It describes a multidimensional attribute value distribution and may be estimated using the known approach based on a superposition of Gaussian clusters. But there is the problem of an efficient integration of the multivariate PDF. This may be solved by applying Monte Carlo (MC) method which exposes its advantages for high dimensions. To satisfy the time constraint of selectivity calculation, the parallelized MC integration method was proposed in the paper. The implementation of the method is based on CUDA technology. The paper also describes the application designated for obtaining the time-optimal parameter values of the method.",

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Augustyn, DR & Warchal, L 2014, Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment. in T Cerquitelli, S Chiusano, A Vakali, B Novikov, J Pokorný, B Catania, G Guerrini, A Kemper, T Palpanas & M Kämpf (eds), New Trends in Databases and Information Systems - Selected Papers of the 17th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events. Advances in Intelligent Systems and Computing, vol. 241, Springer Verlag, pp. 215-224, 17th East European Conference on Advances in Databases and Information Systems, ADBIS 2013, Genoa, Italy, 1/09/13. https://doi.org/10.1007/978-3-319-01863-8_24

Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment. / Augustyn, Dariusz Rafal; Warchal, Lukasz.
New Trends in Databases and Information Systems - Selected Papers of the 17th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events. ed. / Tania Cerquitelli; Silvia Chiusano; Athena Vakali; Boris Novikov; Jaroslav Pokorný; Barbara Catania; Giovanna Guerrini; Alfons Kemper; Themis Palpanas; Mirko Kämpf. Springer Verlag, 2014. p. 215-224 (Advances in Intelligent Systems and Computing; Vol. 241).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment

AU - Augustyn, Dariusz Rafal

AU - Warchal, Lukasz

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - Query selectivity is a parameter that allows to estimate the size of data satisfying a query condition. For complex range query condition it may be defined as multi integral over a multivariate probability density function (PDF). It describes a multidimensional attribute value distribution and may be estimated using the known approach based on a superposition of Gaussian clusters. But there is the problem of an efficient integration of the multivariate PDF. This may be solved by applying Monte Carlo (MC) method which exposes its advantages for high dimensions. To satisfy the time constraint of selectivity calculation, the parallelized MC integration method was proposed in the paper. The implementation of the method is based on CUDA technology. The paper also describes the application designated for obtaining the time-optimal parameter values of the method.

AB - Query selectivity is a parameter that allows to estimate the size of data satisfying a query condition. For complex range query condition it may be defined as multi integral over a multivariate probability density function (PDF). It describes a multidimensional attribute value distribution and may be estimated using the known approach based on a superposition of Gaussian clusters. But there is the problem of an efficient integration of the multivariate PDF. This may be solved by applying Monte Carlo (MC) method which exposes its advantages for high dimensions. To satisfy the time constraint of selectivity calculation, the parallelized MC integration method was proposed in the paper. The implementation of the method is based on CUDA technology. The paper also describes the application designated for obtaining the time-optimal parameter values of the method.

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KW - Data clustering

KW - Monte carlo integration

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BT - New Trends in Databases and Information Systems - Selected Papers of the 17th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events

A2 - Cerquitelli, Tania

A2 - Chiusano, Silvia

A2 - Vakali, Athena

A2 - Novikov, Boris

A2 - Pokorný, Jaroslav

A2 - Catania, Barbara

A2 - Guerrini, Giovanna

A2 - Kemper, Alfons

A2 - Palpanas, Themis

A2 - Kämpf, Mirko

PB - Springer Verlag

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Y2 - 1 September 2013 through 4 September 2013

ER -

Augustyn DR, Warchal L. Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment. In Cerquitelli T, Chiusano S, Vakali A, Novikov B, Pokorný J, Catania B, Guerrini G, Kemper A, Palpanas T, Kämpf M, editors, New Trends in Databases and Information Systems - Selected Papers of the 17th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events. Springer Verlag. 2014. p. 215-224. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-01863-8_24

Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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