Titlebook: Data-Enabled Analytics; DEA for Big Data Joe Zhu,Vincent Charles Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive

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Introduction on Nye’s Theory of Soft Powerved once for each DMU. In data enabled analytics, when a large-scale dataset is evaluated, the elapsed time to apply a DEA model substantially increases. Parallel processing allows splitting the task into several parts so each part can simultaneously be executed on different processors. This study e

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https://doi.org/10.1007/978-981-13-3474-0ttributes and sub attributes in a hierarchical setting. The objective of this chapter is to examine global food security performance using H-DEA model and later uses multi-level K means clustering approach to cluster sampled countries into homogeneous and distinct groups. Under proposed H-DEA with c

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Magda Pischetola,Lone Dirckinck-Holmfeldely used for data analytics of multidimensional datasets. This is particularly so when hybridized with complex network analysis tools. One such hybridization is Dominance Network Analysis, which is based on representing the data through the dominance relationships between the observations. The analy

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Morten Winther Bülow,Rikke Toft N?rg?rdFs against peers also do receive considerable attention in MF performance appraisal literature. Evidence of data envelopment analysis (DEA) playing a big part here is increasing. DEA appraises performance in a multidimensional framework and can accommodate data from different sources and in differen

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