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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978-3-030-75164-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

中古

Joe Zhu,Vincent CharlesExplores novel uses of Data Envelopment Analysis and Big Data.Introduces DEA as a data mining tool, under the big data umbrella.Exams DEA models beyond their present scope and mine new insights for be

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The Estimation of Productive Efficiency Through Machine Learning Techniques: Efficiency Analysis Tr to production theory and engineering. Many parametric and nonparametric approaches have been introduced in the last forty years for estimating production frontiers given a data sample. However, few of these methodologies are based on machine learning techniques, despite being a growing field of res

prosperity

Hybrid Data Science and Reinforcement Learning in Data Envelopment Analysis,e functional form identification of the production frontier and the RL derives the optimal resource reallocation policy which guides the productivity improvement. In fact, both DS and RL techniques complement efficiency analysis. Emphasizes on planning over evaluation, we use data generating process

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Parallel Processing and Large-Scale Datasets in Data Envelopment Analysis,ved 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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