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Data-Enabled Analytics

DEA for Big Data

  • Book
  • © 2021

Overview

Editors:
  1. Joe Zhu

    1. Foisie Business School, Worcester Polytechnic Institute, Worcester, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Vincent Charles

    1. Buckingham Business School, University of Buckingham, Birmingham, UK

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    You can also search for this editor in PubMed   Google Scholar

  • Explores 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 better data-driven decision-making

Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 312)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-x
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  2. Data Envelopment Analysis and Big Data: A Systematic Literature Review with Bibliometric Analysis

    • Vincent Charles, Tatiana Gherman, Joe Zhu
    Pages 1-29

  3. Acceleration of Large-Scale DEA Computations Using Random Forest Classification

    • Anyu Yu, Yu Shi, Joe Zhu
    Pages 31-49

  4. The Estimation of Productive Efficiency Through Machine Learning Techniques: Efficiency Analysis Trees

    • Juan Aparicio, Miriam Esteve, Jesus J. Rodriguez-Sala, Jose L. Zofio
    Pages 51-92

  5. Hybrid Data Science and Reinforcement Learning in Data Envelopment Analysis

    • Chia-Yen Lee, Yu-Hsin Hung, Yen-Wen Chen
    Pages 93-122

  6. Aggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives

    • Bao Hoang Nguyen, Valentin Zelenyuk
    Pages 123-158

  7. Parallel Processing and Large-Scale Datasets in Data Envelopment Analysis

    • Dariush Khezrimotlagh
    Pages 159-174

  8. Network DEA and Big Data with an Application to the Coronavirus Pandemic

    • Hirofumi Fukuyama, William L. Weber
    Pages 175-197

  9. Hierarchical Data Envelopment Analysis for Classification of High-Dimensional Data

    • Ming-Miin Yu, Kok Fong See, Bo Hsiao
    Pages 199-229

  10. Dominance Network Analysis: Hybridizing Dea and Complex Networks for Data Analytics

    • L. Calzada-Infante, S. Lozano
    Pages 231-262

  11. Value Extracting in Relative Performance Appraisal with Network DEA: An Application to U.S. Equity Mutual Funds

    • Hirofumi Fukuyama, Don U. A. Galagedera
    Pages 263-297

  12. Measuring Chinese Bank Performance with Undesirable Outputs: A Slack-Based Two-Stage Network DEA Approach

    • Ya Chen, Mengyuan Wang, Jingyu Yang
    Pages 299-326

  13. Using Network DEA and Grey Prediction Model for Big Data Analysis: An Application in the Global Airline Efficiency

    • Wen-Min Lu, Qian Long Kweh, Mohammad Nourani, Hsiu-Fei Wang
    Pages 327-356

  14. Back Matter

    Pages 357-364
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Keywords

About this book

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.

Editors and Affiliations

  • Foisie Business School, Worcester Polytechnic Institute, Worcester, USA

    Joe Zhu

  • Buckingham Business School, University of Buckingham, Birmingham, UK

    Vincent Charles

About the editors

Joe Zhu is a Professor of Operations Analytics in the Foisie Business School, Worcester Polytechnic Institute. He is an internationally recognized expert in methods of performance evaluation and benchmarking using Data Envelopment Analysis (DEA), and his research interests are in the areas of operations and business analytics, productivity modeling, and performance evaluation and benchmarking. He has published and co-edited several books focusing on performance evaluation and benchmarking using DEA and developed the DEAFrontier software. With more than 130 journal articles, books, and textbooks along with over 20,000 Google Scholar citations, he is recognized as one of the top authors in DEA with respect to research productivity, h-index, and g-index.

Vincent Charles is a Professor of Management Science and the Director of Research at Buckingham Business School, University of Buckingham, UK. He has published over 110 research works with Pearson Education, Cambridge ScholarsPublishing, UK and other publishers. His area of research includes productivity, quality, efficiency, effectiveness, competitiveness, innovation, and design thinking. He has the following industry exposure for research and consultancy purposes:  advertising, agriculture & agribusiness, transportation, consumer products, banking, education, electronics, and manufacturing.

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