Efficient aggregation algorithms for compressed data warehouses

Jianzhong Li, Jaideep Srivastava

Research output: Contribution to journalArticle

32 Citations (Scopus)


Aggregation and cube are important operations for online analytical processing (OLAP). Many efficient algorithms to compute aggregation and cube for relational OLAP have been developed. Some work has been done on efficiently computing cube for multidimensional data warehouses that store data sets in multidimensional arrays rather than in tables. However, to our knowledge, there is nothing to date in the literature describing aggregation algorithms on compressed data warehouses for multidimensional OLAP. This paper presents a set of aggregation algorithms on compressed data warehouses for multidimensional OLAP. These algorithms operate directly on compressed data sets, which are compressed by the mapping-complete compression methods, without the need to first decompress them. The algorithms have different performance behaviors as a function of the data set parameters, sizes of outputs and main memory availability. The algorithms are described and the I/O and CPU cost functions are presented in this paper. A decision procedure to select the most efficient algorithm for a given aggregation request is also proposed. The analysis and experimental results show that the algorithms have better performance on sparse data than the previous aggregation algorithms.

Original languageEnglish
Pages (from-to)515-529
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number3
Publication statusPublished - 1 May 2002



  • Aggregation
  • Aggregation on compressed data warehouses
  • Data warehouse
  • Multidimensional array
  • OLAP

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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