Data-enabled analytics: DEA for big data

Joe Zhu; Vincent Charles

doi:10.1007/978-3-030-75162-3

Data-enabled analytics: DEA for big data

Joe Zhu (Editor)
, Vincent Charles (Editor)

Research output: Book/Report › Edited book › peer-review

Abstract

This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.

Original language	English
Publisher	Springer Cham
Number of pages	374
Volume	312
Edition	1
ISBN (Electronic)	9783030751623
ISBN (Print)	9783030751616, 9783030751647
DOIs	https://doi.org/10.1007/978-3-030-75162-3
Publication status	Published - 17 Dec 2021
Externally published	Yes

Publication series

Name	International Series in Operations Research & Management Science
Volume	312
ISSN (Print)	0884-8289
ISSN (Electronic)	2214-7934

Keywords

Artificial Intelligence
Machine Learning
Data-Enabled Analytics
Data Science
Productivity

ASJC Scopus subject areas

Management Science and Operations Research

Access to Document

10.1007/978-3-030-75162-3Licence: Unspecified

Cite this

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KW - Data Science

KW - Productivity

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Data-enabled analytics: DEA for big data

Abstract

Publication series

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this