Big Data and Information Theory

1. Auflage 2024
ISBN: 978-1-032-26632-9
Verlag: Routledge

Buch, Englisch, 128 Seiten, Format (B × H): 210 mm x 297 mm, Gewicht: 359 g

Big Data and Information Theory are a binding force between various areas of knowledge that allow for societal advancement. Rapid development of data analytic and information theory allows companies to store vast amounts of information about production, inventory, service, and consumer activities. More powerful CPUs and cloud computing make it possible to do complex optimization instead of using heuristic algorithms, as well as instant rather than offline decision-making.

The era of "big data" challenges includes analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. Big data calls for better integration of optimization, statistics, and data mining. In response to these challenges this book brings together leading researchers and engineers to exchange and share their experiences and research results about big data and information theory applications in various areas. This book covers a broad range of topics including statistics, data mining, data warehouse implementation, engineering management in large-scale infrastructure systems, data-driven sustainable supply chain network, information technology service offshoring project issues, online rumors governance, preliminary cost estimation, and information system project selection.

The chapters in this book were originally published in the journal, International Journal of Management Science and Engineering Management.

Zielgruppe

Postgraduate and Undergraduate

Autoren/Hrsg.

Xu, Jiuping

Ahmed, Syed Ejaz

Li, Zongmin

Fachgebiete

• Wirtschaftswissenschaften Wirtschaftswissenschaften Wirtschaftswissenschaften: Allgemeines

Inhaltsverzeichnis

Preface 1. Engineering management: new advances and three open questions 2. Bayes and big data: the consensus Monte Carlo algorithm 3. Measurement and analysis of quality of life related to environmental hazards: the methodology illustrated by recent epidemiological studies 4. Big data analytics: integrating penalty strategies 5. Seeking relationships in big data: a Bayesian perspective 6. Designing a data-driven leagile sustainable closed-loop supply chain network 7. Exploring capability maturity models and relevant practices as solutions addressing information technology service offshoring project issues 8. The evolution and governance of online rumors during the public health emergency: taking COVID-19 pandemic related rumors as an example 9. An empirical study of data warehouse implementation effectiveness 10. Developing a preliminary cost estimation model for tall buildings based on machine learning 11. A framework for managing uncertainty in information system project selection: an intelligent fuzzy approach

Über Autor(innen)

Jiuping Xu is Associate Vice President, Dean of Business School, and Director of Institute of Emergency Management and Reconstruction in Post-disaster of Sichuan University, Chengdu, China. He has published more than 700 peer-reviewed journal papers and over 40 books.

Syed Ejaz Ahmed is Dean of the Faculty of Mathematics and Science at Brock University, St Catharines, Canada. His research interests concentrate on big data, predictive modeling, data science, and statistical machine learning with applications.

Zongmin Li is Deputy Department Head of Management Science and System Science Department of Business School at Sichuan University, Chengdu, China. Her research interests focus on data-driven decision-making and big data analytics.