Read the full paper: Zhu, Joe, DEA under big data: data enabled analytics and network data envelopment analysis,Īnnals of Operations Research, (in press) This represents opportunities for developing techniques for solving non-linear network DEA models. Unlike the conventional DEA that are solved via linear programming, general network DEA corresponds to nonconvex optimization problems. These network structures are too large or complex to be dealt with by the conventional DEA. Network DEA is big Data Enabled Analysis (big DEA) of data when multiple (performance) metrics or attributesĪre linked through network structures. It is shown that network DEA is different from the standard DEA, although it bears the name of DEA and some similarity These network structures encompass a broader range of metrics that cannot be modelled by the conventional DEA. Valuable information hidden in big data that are represented by network structures should be extracted by DEA. To deal with large volume of data (decision making units, inputs, and outputs) under the conventional DEA, While computational algorithms have been developed Data envelopment analysis (DEA) should be viewed as a method (or tool) for data-oriented analytics.ĭEA is a data-driven tool for performance evaluation and benchmarking.
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