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Impact of the Union and Difference Operations on the Quality of Information Products

Author

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  • Amir Parssian

    (Department of Information Systems, Instituto de Empresa Business School, Madrid 28006, Spain)

  • Sumit Sarkar

    (School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Varghese S. Jacob

    (School of Management, University of Texas at Dallas, Richardson, Texas 75080)

Abstract

Information derived from relational databases is routinely used for decision making. However, little thought is usually given to the quality of the source data, its impact on the quality of the derived information, and how this in turn affects decisions. To assess quality, one needs a framework that defines relevant metrics that constitute the quality profile of a relation, and provides mechanisms for their evaluation. We build on a quality framework proposed in prior work, and develop quality profiles for the result of the primitive relational operations Difference and Union. These operations have nuances that make both the classification of the resulting records as well as the estimation of the different classes quite difficult to address, and very different from that for other operations. We first determine how tuples appearing in the results of these operations should be classified as accurate, inaccurate or mismember, and when tuples that should appear do not (called incomplete) in the result. Although estimating the cardinalities of these subsets directly is difficult, we resolve this by decomposing the problem into a sequence of drawing processes, each of which follows a hyper-geometric distribution. Finally, we discuss how decisions would be influenced based on the resulting quality profiles.

Suggested Citation

  • Amir Parssian & Sumit Sarkar & Varghese S. Jacob, 2009. "Impact of the Union and Difference Operations on the Quality of Information Products," Information Systems Research, INFORMS, vol. 20(1), pages 99-120, March.
    • Handle: RePEc:inm:orisre:v:20:y:2009:i:1:p:99-120
    DOI: 10.1287/isre.1070.0161
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    References listed on IDEAS

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    1. Kon, Henry B. & Madnick, Stuart E. & Siegel, Michael D., 1995. "Good answers from bad data : a data management strategy," Working papers 3868-95., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. Craig W. Fisher & InduShobha Chengalur-Smith & Donald P. Ballou, 2003. "The Impact of Experience and Time on the Use of Data Quality Information in Decision Making," Information Systems Research, INFORMS, vol. 14(2), pages 170-188, June.
    3. Donald Ballou & Richard Wang & Harold Pazer & Giri Kumar Tayi, 1998. "Modeling Information Manufacturing Systems to Determine Information Product Quality," Management Science, INFORMS, vol. 44(4), pages 462-484, April.
    4. Amir Parssian & Sumit Sarkar & Varghese S. Jacob, 2004. "Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian Product," Management Science, INFORMS, vol. 50(7), pages 967-982, July.
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    Cited by:

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    2. Debabrata Dey & Subodha Kumar, 2013. "Data Quality of Query Results with Generalized Selection Conditions," Operations Research, INFORMS, vol. 61(1), pages 17-31, February.

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