Data-Enabled Analytics DEA for Big Data /

Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Zhu, Joe (Editor), Charles, Vincent (Editor)
Summary:	X, 364 p. 103 illus. text
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:	1st ed. 2021.
Series:	International Series in Operations Research & Management Science, 312
Subjects:	Operations research. Business information services. Econometrics. Big data. Economics. Operations Research and Decision Theory. IT in Business. Quantitative Economics. Big Data.
Online Access:	https://doi.org/10.1007/978-3-030-75162-3
Format:	Electronic Book

Table of Contents:

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.

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