Data-Enabled Analytics
DEA for Big Data
Published on 16. December 2021
X, 364 pages
978-3-030-75162-3 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
X, 364 p. 103 illus.
File size
7,90 MB
ISBN-13
978-3-030-75162-3 (9783030751623)
DOI
10.1007/978-3-030-75162-3
Schweitzer Classification
Other editions
Additional editions
Book
12/2021
Springer
€160.49
Shipment within 7-9 days
Persons
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.
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.
Content
Chapter 1. Data Envelopment Analysis and Big Data: A Systematic Literature Review with Bibliometric Analysis.- Chapter 2. Acceleration of large-scale DEA computations using random forest classification.- Chapter 3. The estimation of productive efficiency through machine learning techniques: Efficiency Analysis Trees.- Chapter 4. Hybrid Data Science and Reinforcement Learning in Data Envelopment Analysis.- Chapter 5. Aggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives.- Chapter 6. Parallel Processing and Large-Scale Datasets in Data Envelopment Analysis.- Chapter 7. Network DEA and Big Data with an Application to the Coronavirus Pandemic.- Chapter 8. Hierarchical Data Envelopment Analysis for Classification of High-Dimensional Data.- Chapter 9. Dominance Network Analysis: Hybridizing DEA and Complex Networks for Data Analytics.- Chapter 10. Value extracting in relative performance appraisal with networkDEA: an application to U.S. equity mutual funds.- Chapter 11. Measuring Chinese bank performance with undesirable outputs: a slack-based two-stage network DEA approach.- Chapter 12. Using Network DEA and Grey Prediction Model for Big Data Analysis: An Application in the Global Airline Efficiency.
