Data-Enabled Analytics

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 Scholars Publishing, 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.