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Data Quality of Query Results with Generalized Selection Conditions

Author

Listed:
  • Debabrata Dey

    (Foster School of Business, University of Washington, Seattle, Washington 98195)

  • Subodha Kumar

    (Mays Business School, Texas A&M University, College Station, Texas 77845)

Abstract

Information systems play a very important role in managerial decision making within modern organizations. While making different types of decisions (at operational, tactical, and strategic levels), managers are increasingly relying on information gleaned from various databases, data warehouses, and data streams feeding them. The quality of organizational decisions, therefore, often depends on the quality of the information derived from these databases and data streams, and a manager is able to make better use of the information if she also understands the quality level of that information. Previous research has examined how the quality level of a database query output can be estimated based on the quality level of the input data. In this research, we generalize this stream of research and allow a query to have general selection conditions involving multiple attributes with any combination of conjunction or disjunction of subconditions that may include functions of multiple attributes. Results of this research can easily be implemented in real-world decision contexts.

Suggested Citation

  • Debabrata Dey & Subodha Kumar, 2013. "Data Quality of Query Results with Generalized Selection Conditions," Operations Research, INFORMS, vol. 61(1), pages 17-31, February.
    • Handle: RePEc:inm:oropre:v:61:y:2013:i:1:p:17-31
    DOI: 10.1287/opre.1120.1128
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    References listed on IDEAS

    as
    1. Debabrata Dey, 2003. "Record Matching in Data Warehouses: A Decision Model for Data Consolidation," Operations Research, INFORMS, vol. 51(2), pages 240-254, April.
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    5. repec:mpr:mprres:3857 is not listed on IDEAS
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    7. 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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    9. Debabrata Dey & Sumit Sarkar & Prabuddha De, 1998. "A Probabilistic Decision Model for Entity Matching in Heterogeneous Databases," Management Science, INFORMS, vol. 44(10), pages 1379-1395, October.
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    12. 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.
    13. Debabrata Dey & Subodha Kumar, 2010. "Reassessing Data Quality for Information Products," Management Science, INFORMS, vol. 56(12), pages 2316-2322, December.
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    Cited by:

    1. Qi Liu & Gengzhong Feng & Giri Kumar Tayi & Jun Tian, 2021. "Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach," Information Systems Frontiers, Springer, vol. 23(2), pages 375-389, April.
    2. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    3. Lu, Jizhou & Feng, Gengzhong & Shum, Stephen & Lai, Kin Keung, 2021. "On the value of information sharing in the presence of information errors," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1139-1152.

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