Regression cubes with lossless compression and aggregation

Yixin Chen, Guozhu Dong, Jiawei Han, Jian Pei, Benjamin W. Wah, Jianyong Wang

Research output: Contribution to journalArticlepeer-review


As OLAP engines are widely used to support multidimensional data analysis, it is desirable to support in data cubes advanced statistical measures, such as regression and filtering, in addition to the traditional simple measures such as count and average. Such new measures will allow users to model, smooth, and predict the trends and patterns of data. Existing algorithms for simple distributive and algebraic measures are inadequate for efficient computation of statistical measures in a multidimensional space. In this paper, we propose a fundamentally new class of measures, compressible measures, in order to support efficient computation of the statistical models. For compressible measures, we compress each cell into an auxiliary matrix with a size independent of the number of tuples. We can then compute the statistical measures for any data cell from the compressed data of the lower-level cells without accessing the raw data. Time- and space-efficient lossless aggregation formulae are derived for regression and filtering measures. Our analytical and experimental studies show that the resulting system, regression cube, substantially reduces the memory usage and the overall response time for statistical analysis of multidimensional data.

Original languageEnglish (US)
Article number1717417
Pages (from-to)1585-1598
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number12
StatePublished - Dec 2006


  • Aggregation
  • Compression
  • Data cubes
  • OLAP

ASJC Scopus subject areas

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


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