High performance data mining using data cubes on parallel computers

Sanjay Goil; Alok Nidhi Choudhary

doi:10.1109/IPPS.1998.669979

High performance data mining using data cubes on parallel computers

Sanjay Goil^*, Alok Nidhi Choudhary

^*Corresponding author for this work

Electrical and Computer Engineering

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

5 Scopus citations

Abstract

On-Line Analytical Processing techniques are used for data analysis and decision support systems. The multidimensionality of the underlying data is well represented by multidimensional databases. For data mining in knowledge discovery, OLAP calculations can be effectively used. For these, high performance parallel systems are required to provide interactive analysis. Precomputed aggregate calculations in a Data Cube can provide efficient query processing for OLAP applications. In this article, we present parallel data cube construction on distributed-memory parallel computers from a relational database. Data Cube is used for data mining of associations using Attribute Focusing. Results are presented for these on the IBM-SP2, which show that our algorithms and techniques are scalable to a large number of processors, providing a high performance platform for such applications.

Original language	English (US)
Title of host publication	Proceedings of the International Parallel Processing Symposium, IPPS
Editors	Anon
Pages	548-555
Number of pages	8
DOIs	https://doi.org/10.1109/IPPS.1998.669979
State	Published - Jan 1 1998
Event	Proceedings of the 1998 12th International Parallel Processing Symposium and 9th Symposium on Parallel and Distributed Processing - Orlando, FL, USA Duration: Mar 30 1998 → Apr 3 1998

Publication series

Name	Proceedings of the International Parallel Processing Symposium, IPPS
ISSN (Print)	1063-7133

Other

Other	Proceedings of the 1998 12th International Parallel Processing Symposium and 9th Symposium on Parallel and Distributed Processing
City	Orlando, FL, USA
Period	3/30/98 → 4/3/98

ASJC Scopus subject areas

Hardware and Architecture

Access to Document

10.1109/IPPS.1998.669979

Fingerprint Dive into the research topics of 'High performance data mining using data cubes on parallel computers'. Together they form a unique fingerprint.

Cite this

@inproceedings{08cb97c5bd754da4933e1cf5a355f06e,

title = "High performance data mining using data cubes on parallel computers",

abstract = "On-Line Analytical Processing techniques are used for data analysis and decision support systems. The multidimensionality of the underlying data is well represented by multidimensional databases. For data mining in knowledge discovery, OLAP calculations can be effectively used. For these, high performance parallel systems are required to provide interactive analysis. Precomputed aggregate calculations in a Data Cube can provide efficient query processing for OLAP applications. In this article, we present parallel data cube construction on distributed-memory parallel computers from a relational database. Data Cube is used for data mining of associations using Attribute Focusing. Results are presented for these on the IBM-SP2, which show that our algorithms and techniques are scalable to a large number of processors, providing a high performance platform for such applications.",

author = "Sanjay Goil and Choudhary, {Alok Nidhi}",

year = "1998",

month = jan,

day = "1",

doi = "10.1109/IPPS.1998.669979",

language = "English (US)",

isbn = "0818684046",

series = "Proceedings of the International Parallel Processing Symposium, IPPS",

pages = "548--555",

editor = "Anon",

booktitle = "Proceedings of the International Parallel Processing Symposium, IPPS",

note = "Proceedings of the 1998 12th International Parallel Processing Symposium and 9th Symposium on Parallel and Distributed Processing ; Conference date: 30-03-1998 Through 03-04-1998",

}

Goil, S & Choudhary, AN 1998, High performance data mining using data cubes on parallel computers. in Anon (ed.), Proceedings of the International Parallel Processing Symposium, IPPS. Proceedings of the International Parallel Processing Symposium, IPPS, pp. 548-555, Proceedings of the 1998 12th International Parallel Processing Symposium and 9th Symposium on Parallel and Distributed Processing, Orlando, FL, USA, 3/30/98. https://doi.org/10.1109/IPPS.1998.669979

TY - GEN

T1 - High performance data mining using data cubes on parallel computers

AU - Goil, Sanjay

AU - Choudhary, Alok Nidhi

PY - 1998/1/1

Y1 - 1998/1/1

N2 - On-Line Analytical Processing techniques are used for data analysis and decision support systems. The multidimensionality of the underlying data is well represented by multidimensional databases. For data mining in knowledge discovery, OLAP calculations can be effectively used. For these, high performance parallel systems are required to provide interactive analysis. Precomputed aggregate calculations in a Data Cube can provide efficient query processing for OLAP applications. In this article, we present parallel data cube construction on distributed-memory parallel computers from a relational database. Data Cube is used for data mining of associations using Attribute Focusing. Results are presented for these on the IBM-SP2, which show that our algorithms and techniques are scalable to a large number of processors, providing a high performance platform for such applications.

AB - On-Line Analytical Processing techniques are used for data analysis and decision support systems. The multidimensionality of the underlying data is well represented by multidimensional databases. For data mining in knowledge discovery, OLAP calculations can be effectively used. For these, high performance parallel systems are required to provide interactive analysis. Precomputed aggregate calculations in a Data Cube can provide efficient query processing for OLAP applications. In this article, we present parallel data cube construction on distributed-memory parallel computers from a relational database. Data Cube is used for data mining of associations using Attribute Focusing. Results are presented for these on the IBM-SP2, which show that our algorithms and techniques are scalable to a large number of processors, providing a high performance platform for such applications.

UR - http://www.scopus.com/inward/record.url?scp=0031673733&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031673733&partnerID=8YFLogxK

U2 - 10.1109/IPPS.1998.669979

DO - 10.1109/IPPS.1998.669979

M3 - Conference contribution

AN - SCOPUS:0031673733

SN - 0818684046

T3 - Proceedings of the International Parallel Processing Symposium, IPPS

SP - 548

EP - 555

BT - Proceedings of the International Parallel Processing Symposium, IPPS

A2 - Anon, null

T2 - Proceedings of the 1998 12th International Parallel Processing Symposium and 9th Symposium on Parallel and Distributed Processing

Y2 - 30 March 1998 through 3 April 1998

ER -