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Optimal Reorganization Policies for Stationary and Evolutionary Databases

Author

Listed:
  • June S. Park

    (Department of Management Sciences, The University of Iowa, Iowa City, Iowa 52242)

  • Robert Bartoszynski

    (Department of Statistics, The Ohio State University, Columbus, Ohio 43210)

  • Prabuddha De

    (Department of MIS & Decision Sciences, University of Dayton, Dayton, Ohio 45469)

  • Hasan Pirkul

    (Faculty of Accounting and MIS, The Ohio State University, Columbus, Ohio 43210)

Abstract

The problem of determining optimal reorganization policies for databases which employ file structures with overflow chaining is studied. The dynamics of the file performance driven by update transactions and reorganizations is formulated as a stochastic control model which incorporates micro-level design parameters of the physical file structure. Various simplifying assumptions employed in past research are relaxed in the model. Polynomial time procedures for solving the optimization models are developed for two cases: when the file size is stationary as in the steady-state and when the file size evolves stochastically with a nonlinear trajectory. The model and the solution procedures are applied to an ISAM file revealing the effectiveness of the solution procedures and the relationship between file design parameters and the optimal policy.

Suggested Citation

  • June S. Park & Robert Bartoszynski & Prabuddha De & Hasan Pirkul, 1990. "Optimal Reorganization Policies for Stationary and Evolutionary Databases," Management Science, INFORMS, vol. 36(5), pages 613-631, May.
    • Handle: RePEc:inm:ormnsc:v:36:y:1990:i:5:p:613-631
    DOI: 10.1287/mnsc.36.5.613
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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.

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