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Policies for knowledge refreshing in databases

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  • Fang, Xiao
  • Rachamadugu, Ram

Abstract

Knowledge discovery in databases (KDD) provides organizations necessary tools to sift through vast data stores to extract knowledge. This process supports and improves decision making in organizations. In this paper, we introduce and define the concept of knowledge refreshing, a critical step to ensure the quality and timeliness of knowledge discovered in a KDD process. This has been unfortunately overlooked by prior researchers. Specifically, we study knowledge refreshing from the perspective of when to refresh knowledge so that the total system cost over a time horizon is minimized. We propose a model for knowledge refreshing, and a dynamic programming methodology for developing optimal strategies. We demonstrate the effectiveness of the proposed methodology using data from a real world application. The proposed methodology provides decision makers guidance in running KDD effectively and efficiently.

Suggested Citation

  • Fang, Xiao & Rachamadugu, Ram, 2009. "Policies for knowledge refreshing in databases," Omega, Elsevier, vol. 37(1), pages 16-28, February.
    • Handle: RePEc:eee:jomega:v:37:y:2009:i:1:p:16-28
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    Cited by:

    1. Xiao Fang & Olivia R. Liu Sheng & Paulo Goes, 2013. "When Is the Right Time to Refresh Knowledge Discovered from Data?," Operations Research, INFORMS, vol. 61(1), pages 32-44, February.
    2. Sundararaghavan, P.S. & Kunnathur, Anand & Fang, Xiao, 2010. "Sequencing questions to ferret out terrorists: Models and heuristics," Omega, Elsevier, vol. 38(1-2), pages 12-19, February.

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