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International Conference on Data Warehousing and Knowledge Discovery

DaWaK 2002: Data Warehousing and Knowledge Discovery pp 286–295Cite as

Modeling and Imputation of Large Incomplete Multidimensional Datasets

Part of the Lecture Notes in Computer Science book series (LNCS,volume 2454)

Abstract

The presence of missing or incomplete data is a commonplace in large real-word databases. In this paper, we study the problem of missing values which occur at the measure dimension of data cube. We propose a two-part mixture model, which combines the logistic model and loglinear model together, to predict and impute the missing values. The logistic model here is applied to predict missing positions while the loglinear model is applied to compute the estimation. Experimental results on real datasets and synthetic datasets are presented.

Keywords

  • Logistic Model
  • Range Query
  • Synthetic Dataset
  • Loglinear Model
  • Data Cube

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The datacube is a widely used data model for On-Line Analytical Processing (OLAP).A datacube is a multidimensional data abstraction, where aggregated measures of the combinations of dimension values are kept.

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Author information

Authors and Affiliations

  1. University of North Carolina at Charlotte, North Carolina

    Xintao Wu

  2. George Mason University, Virginia

    Daniel Barbará

Authors
  1. Xintao Wu
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  2. Daniel Barbará
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Editor information

Editors and Affiliations

  1. Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, 606-8501, Kyoto, Japan

    Yahiko Kambayashi

  2. Institute for Computer Science and Business Informatics, University of Vienna, Liebiggasse 4, 1010, Vienna, Austria

    Werner Winiwarter

  3. Center for Spatial Information Science (CSIS), University of Tokyo, 4-6-1, Komaba, Meguro-ku, 153-8904, Tokyo, Japan

    Masatoshi Arikawa

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© 2002 Springer-Verlag Berlin Heidelberg

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Cite this paper

Wu, X., Barbará, D. (2002). Modeling and Imputation of Large Incomplete Multidimensional Datasets. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2002. Lecture Notes in Computer Science, vol 2454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46145-0_28

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  • DOI: https://doi.org/10.1007/3-540-46145-0_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44123-6

  • Online ISBN: 978-3-540-46145-6

  • eBook Packages: Springer Book Archive

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