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Regression cubes with lossless compression and aggregation

Publication ,  Journal Article
Chen, Y; Dong, G; Han, J; Pei, J; Wah, BW; Wang, J
Published in: IEEE Transactions on Knowledge and Data Engineering
December 1, 2006

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. © 2006 IEEE.

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Published In

IEEE Transactions on Knowledge and Data Engineering

DOI

ISSN

1041-4347

Publication Date

December 1, 2006

Volume

18

Issue

12

Start / End Page

1585 / 1598

Related Subject Headings

  • Information Systems
  • 46 Information and computing sciences
  • 08 Information and Computing Sciences
 

Citation

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Chen, Y., Dong, G., Han, J., Pei, J., Wah, B. W., & Wang, J. (2006). Regression cubes with lossless compression and aggregation. IEEE Transactions on Knowledge and Data Engineering, 18(12), 1585–1598. https://doi.org/10.1109/TKDE.2006.196
Chen, Y., G. Dong, J. Han, J. Pei, B. W. Wah, and J. Wang. “Regression cubes with lossless compression and aggregation.” IEEE Transactions on Knowledge and Data Engineering 18, no. 12 (December 1, 2006): 1585–98. https://doi.org/10.1109/TKDE.2006.196.
Chen Y, Dong G, Han J, Pei J, Wah BW, Wang J. Regression cubes with lossless compression and aggregation. IEEE Transactions on Knowledge and Data Engineering. 2006 Dec 1;18(12):1585–98.
Chen, Y., et al. “Regression cubes with lossless compression and aggregation.” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 12, Dec. 2006, pp. 1585–98. Scopus, doi:10.1109/TKDE.2006.196.
Chen Y, Dong G, Han J, Pei J, Wah BW, Wang J. Regression cubes with lossless compression and aggregation. IEEE Transactions on Knowledge and Data Engineering. 2006 Dec 1;18(12):1585–1598.

Published In

IEEE Transactions on Knowledge and Data Engineering

DOI

ISSN

1041-4347

Publication Date

December 1, 2006

Volume

18

Issue

12

Start / End Page

1585 / 1598

Related Subject Headings

  • Information Systems
  • 46 Information and computing sciences
  • 08 Information and Computing Sciences