Big Data And Information Theory

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.

Objective Information Theory (OIT) is proposed to represent and compute the information in a large-scale complex information system with big data in this monograph. To formally analyze, design, develop, and evaluate the information, OIT interprets the information from essential nature, measures the information from mathematical properties, and models the information from concept, logic, and physic. As the exemplified applications, Air Traffic Control System (ATCS) and Smart Court SoSs (System of Systems) are introduced for practical OITs. This Open Access book can be used as a technical reference book in the field of information science and also a reference textbook for senior students and graduate ones in related majors.

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Quantifying the Qualitative presents a systematic approach to comparative case analysis based on insights from information theory. This new method, which requires minimal quantitative skills, helps students, policymakers, professionals, and scholars learn more from comparative cases. The approach avoids the limitations of traditional statistics in the small-n context and allows analysts to systematically assess and compare the impact of a set of factors on case outcomes with easy-to-use analytics. Rigorous tools reduce bias, improve the knowledge gained from case studies, and provide straightforward metrics for effectively communicating results to a range of readers and leaders.

The concept of Information is to disseminate scientific results achieved via experiments and theoretical results in depth. It is very important to enable researchers and practitioners to learn new technology and findings that enable development in the applied field.

In September 2018, researchers from Armenia, Chile, Germany and Japan met in Yerevan to discuss technologies with applications in Smart Cities, Data Science and Information-Theoretic Approaches for Smart Systems, Technical Challenges for Smart Environments, and Smart Human Centered Computing. This book presents their contributions to the CODASSCA 2018 workshop on Collaborative Technologies and Data Science in Smart City Applications, a cutting-edge topic in Computer Science today.

Examines the crucial interaction between big data and communication, social and biological networks using critical mathematical tools and state-of-the-art research.

This book constitutes the refereed post-conference proceedings of the Second BenchCouncil International Federated Intelligent Computing and Block Chain Conferences, FICC 2020, held in Qingdao, China, in October/ November 2020. The 32 full papers and 6 short papers presented were carefully reviewed and selected from 103 submissions. The papers of this volume are organized in topical sections on AI and medical technology; AI and big data; AI and block chain; AI and education technology; and AI and financial technology.