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DEA with streaming data

Author

Listed:
  • Dulá, J.H.
  • López, F.J.

Abstract

DEA can be interpreted as a tool for the identification of “frontier outliers” among data points. These are points that are potentially interesting because they exhibit extreme properties in that the values of their attributes, either alone or combined, are at the upper or lower limits of the data set to which they belong. A real challenge for this type of frontier analysis arises when data stream in at high rates and the DEA analysis needs to be performed quickly. This paper extends DEA into this dynamic data environment. The purpose is to propose a formal theoretical framework to handle streaming data and to answer the question of how fast data can be processed using this new framework. Potential applications involving large data sets include auditing, appraisals, fraud detection, and security. In such settings the situation is likely to be dynamic with the data domain constantly changing as new entities arrive in the course of time. New specialized tools to adapt DEA to deal with streaming data will be explored.

Suggested Citation

  • Dulá, J.H. & López, F.J., 2013. "DEA with streaming data," Omega, Elsevier, vol. 41(1), pages 41-47.
  • Handle: RePEc:eee:jomega:v:41:y:2013:i:1:p:41-47
    DOI: 10.1016/j.omega.2011.07.010
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    References listed on IDEAS

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    1. Cooper, William W. & Ruiz, Jose L. & Sirvent, Inmaculada, 2007. "Choosing weights from alternative optimal solutions of dual multiplier models in DEA," European Journal of Operational Research, Elsevier, vol. 180(1), pages 443-458, July.
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    4. Po, Rung-Wei & Guh, Yuh-Yuan & Yang, Miin-Shen, 2009. "A new clustering approach using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 199(1), pages 276-284, November.
    5. Ali, Agha Iqbal, 1993. "Streamlined computation for data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 64(1), pages 61-67, January.
    6. Cook, Wade D. & Liang, Liang & Zhu, Joe, 2010. "Measuring performance of two-stage network structures by DEA: A review and future perspective," Omega, Elsevier, vol. 38(6), pages 423-430, December.
    7. J.H. Dulá & R.M. Thrall, 2001. "A Computational Framework for Accelerating DEA," Journal of Productivity Analysis, Springer, vol. 16(1), pages 63-78, July.
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    Cited by:

    1. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.

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