High performance data mining using data cubes on parallel computers

Sanjay Goil*, Alok Nidhi Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationProceedings of the International Parallel Processing Symposium, IPPS
Editors Anon
Pages548-555
Number of pages8
DOIs
StatePublished - Jan 1 1998
EventProceedings of the 1998 12th International Parallel Processing Symposium and 9th Symposium on Parallel and Distributed Processing - Orlando, FL, USA
Duration: Mar 30 1998Apr 3 1998

Publication series

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

Other

OtherProceedings of the 1998 12th International Parallel Processing Symposium and 9th Symposium on Parallel and Distributed Processing
CityOrlando, FL, USA
Period3/30/984/3/98

ASJC Scopus subject areas

  • Hardware and Architecture

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