Data-Enabled Analytics

DEA for Big Data

Häftad, Engelska, 2022

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This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. 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.

Produktinformation

Utgivningsdatum2022-12-18
Mått155 x 235 x 21 mm
Vikt569 g
FormatHäftad
SpråkEngelska
SerieInternational Series in Operations Research & Management Science
Antal sidor364
FörlagSpringer Nature Switzerland AG
ISBN9783030751647

Tillhör följande kategorier

Företagsekonomi inom Ekonomi och ledarskap
Nationalekonomi inom Ekonomi och ledarskap
Ledarskap och motivation inom Ekonomi och ledarskap
Affärsapplikationer inom Data och it
Databaser inom Data och it
Affärsförhandlingar inom Ekonomi och ledarskap

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.

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.