Big Data and Information Theory

Herausgegeben:Xu, Jiuping; Ahmed, Syed Ejaz; Li, Zongmin

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Routledge / Taylor & Francis
• Seitenzahl: 128
• Englisch
• Abmessung: 10mm x 210mm x 297mm
• Gewicht: 400g
• ISBN-13: 9781032266312
• Artikelnr.: 63224142

Inhaltsangabe

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