Design and implementation of a scalable parallel system for multidimensional analysis and OLAP

Sanjay Goil*, Alok Choudhary

*Corresponding author for this work

Research output: Contribution to journalConference article

8 Scopus citations

Abstract

A parallel and scalable infrastructure for on-line analytical processing (OLAP) and multidimensional analysis is presented. Chunking is used to store data either as a dense block using multidimensional arrays or a sparse set using a bit encoded sparse structure (BESS). Chunks provide a multidimensional index structure for efficient dimension oriented data accesses much the same as mid-arrays. Operations within chunks and between chunks are a combination of relational and multi-dimensional operations depending on whether the chunk is sparse or dense. The performance results for data sets with 3, 5 and 10 dimensions for implementation on the IBM SP-2 which show good speedup and scalability are also presented.

Original languageEnglish (US)
Pages (from-to)576-581
Number of pages6
JournalProceedings of the International Parallel Processing Symposium, IPPS
StatePublished - Jan 1 1999
EventProceedings of the 1999 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing - San Juan
Duration: Apr 12 1999Apr 16 1999

ASJC Scopus subject areas

  • Hardware and Architecture

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