DEA with streaming data

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.

► DEA is generalized to apply in a new setting where the data arrives in a stream. ► Examples of applications for this type of frontier analysis are presented. ► A formal theoretical framework is proposed for the new dynamic environment. ► New specialized computational tools to process streaming data are introduced and tested. ► The first computational baselines are established reflecting the state of the art in processing rates for streaming data with DEA.