Parallel scalable infrastructure for OLAP and data mining

Sanjay Goil*, Alok Choudhary

*Corresponding author for this work

Research output: Contribution to conferencePaper

32 Scopus citations

Abstract

A parallel and scalable On-Line Analytical processing (OLAP) and data mining framework for large data sets is presented. The PARSIMONY platform which is used both for OLAP and data mining is described. Sparsity of data sets is handled by using sparse chunks using a bit-encoded sparse structure for compression, which enables aggregate operations on compressed data. Techniques for effectively using summary information available in data cubes for data mining are presented for mining Association rules and decision-tree based Classification.

Original languageEnglish (US)
Pages178-186
Number of pages9
StatePublished - Jan 1 1999
EventProceedings of the 1999 International Database Engineering and Application Symposium, IDEAS'99 - Montreal, Que, Can
Duration: Aug 2 1999Aug 4 1999

Conference

ConferenceProceedings of the 1999 International Database Engineering and Application Symposium, IDEAS'99
CityMontreal, Que, Can
Period8/2/998/4/99

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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    Goil, S., & Choudhary, A. (1999). Parallel scalable infrastructure for OLAP and data mining. 178-186. Paper presented at Proceedings of the 1999 International Database Engineering and Application Symposium, IDEAS'99, Montreal, Que, Can, .