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A selective data retention approach in massive databases

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  • Kalfus, Orly
  • Ronen, Boaz
  • Spiegler, Israel

Abstract

Exponentially growing databases have been tackled on two basic fronts: technological and methodological. Technology offered solution in storage capacity, processing power, and access speed. Among the methodologies are indexing, views, data mining, and temporal databases, and combinations of technology and methodology come in the form of data warehousing, all designed to get the most out of and best handle mounting and complex databases. The basic premise that underlines those approaches is to store everything. We challenge that premise suggesting a selective retention approach for operational data thus curtailing the size of databases and warehouses without losing content and information value. A model and methodology for selective data retention are introduced. The model, using cost/benefit analysis, allows assessing data elements currently stored in the database as well as providing a retention policy regarding current and prospective data. An example case study on commercial data illustrates the model and concepts of such method.

Suggested Citation

  • Kalfus, Orly & Ronen, Boaz & Spiegler, Israel, 2004. "A selective data retention approach in massive databases," Omega, Elsevier, vol. 32(2), pages 87-95, April.
    • Handle: RePEc:eee:jomega:v:32:y:2004:i:2:p:87-95
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    Cited by:

    1. Lila Rao & Kweku-Muata Osei-Bryson, 2008. "An approach for incorporating quality-based cost–benefit analysis in data warehouse design," Information Systems Frontiers, Springer, vol. 10(3), pages 361-373, July.

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