​​​The importance of benchmarking in the service sector is well recognized as it helps in continuous improvement in products and work processes. Through benchmarking, companies have strived to implement best practices in order to remain competitive in the product- market in which they operate. However studies on benchmarking, particularly in the software development sector, have neglected using multiple variables and therefore have not been as comprehensive. Information Theory and Best Practices in the IT Industry fills this void by examining benchmarking in the business of software development and studying how it is affected by development process, application type, hardware platforms used, and many other variables. Information Theory and Best Practices in the IT Industry begins by examining practices of benchmarking productivity and critically appraises them. Next the book identifies different variables which affect productivity and variables that affect quality, developing useful equations that explaining their relationships. Finally these equations and findings are applied to case studies. Utilizing this book, practitioners can decide about what emphasis they should attach to different variables in their own companies, while seeking to optimize productivity and defect density.

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.

CRYPTOGRAPHY, INFORMATION THEORY, AND ERROR-CORRECTION A rich examination of the technologies supporting secure digital information transfers from respected leaders in the field As technology continues to evolve Cryptography, Information Theory, and Error-Correction: A Handbook for the 21ST Century is an indispensable resource for anyone interested in the secure exchange of financial information. Identity theft, cybercrime, and other security issues have taken center stage as information becomes easier to access. Three disciplines offer solutions to these digital challenges: cryptography, information theory, and error-correction, all of which are addressed in this book. This book is geared toward a broad audience. It is an excellent reference for both graduate and undergraduate students of mathematics, computer science, cybersecurity, and engineering. It is also an authoritative overview for professionals working at financial institutions, law firms, and governments who need up-to-date information to make critical decisions. The book’s discussions will be of interest to those involved in blockchains as well as those working in companies developing and applying security for new products, like self-driving cars. With its reader-friendly style and interdisciplinary emphasis this book serves as both an ideal teaching text and a tool for self-learning for IT professionals, statisticians, mathematicians, computer scientists, electrical engineers, and entrepreneurs. Six new chapters cover current topics like Internet of Things security, new identities in information theory, blockchains, cryptocurrency, compression, cloud computing and storage. Increased security and applicable research in elliptic curve cryptography are also featured. The book also: Shares vital, new research in the field of information theory Provides quantum cryptography updates Includes over 350 worked examples and problems for greater understanding of ideas. Cryptography, Information Theory, and Error-Correction guides readers in their understanding of reliable tools that can be used to store or transmit digital information safely.

Business intelligence supports managers in enterprises to make informed business decisions in various levels and domains such as in healthcare. These technologies can handle large structured and unstructured data (big data) in the healthcare industry. Because of the complex nature of healthcare data and the significant impact of healthcare data analysis, it is important to understand both the theories and practices of business intelligence in healthcare. Theory and Practice of Business Intelligence in Healthcare is a collection of innovative research that introduces data mining, modeling, and analytic techniques to health and healthcare data; articulates the value of big volumes of data to health and healthcare; evaluates business intelligence tools; and explores business intelligence use and applications in healthcare. While highlighting topics including digital health, operations intelligence, and patient empowerment, this book is ideally designed for healthcare professionals, IT consultants, hospital directors, data management staff, data analysts, hospital administrators, executives, managers, academicians, students, and researchers seeking current research on the digitization of health records and health systems integration.

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. - Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful - Gain practical understanding of machine learning used in oil and gas operations through contributed case studies - Learn change management skills that will help gain confidence in pursuing the technology - Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

The book “Advances in Information and Communication Networks - Proceedings of the 2022 Future of Information and Communication Conference (FICC)” aims in presenting the latest research advances, sharing expert knowledge and exchanging ideas with the common goal of shaping the future of Information and Communication. The conference attracted 402 submissions, of which, 131 submissions (including six poster papers) have been selected through a double-blind review process by an international panel of expert referees. This book discusses on aspects of Communication, Data Science, Ambient Intelligence, Networking, Computing, Security and Internet of Things, from classical to intelligent scope. The intention is to help academic pioneering researchers, scientists, industrial engineers, and students become familiar with and stay abreast of the ever-changing technology surrounding their industry. We hope that readers find the volume interesting and valuable; it gathers chapters addressing state-of-the-art intelligent methods and techniques for solving real world problems along with a vision of the future research.

Practical tools for analyzing, calculating, and reporting availability, reliability, and maintainability metrics Engineers in the telecommunications industry must be able to quantify system reliability and availability metrics for use in service level agreements, system design decisions, and daily operations. Increasing system complexity and software dependence require new, more sophisticated tools for system modeling and metric calculation than those available in the current literature. Telecommunications System Reliability Engineering, Theory, and Practice provides a background in reliability engineering theory as well as detailed sections discussing applications to fiber optic networks (earth station and space segment), microwave networks (long-haul, cellular backhaul and mobile wireless), satellite networks (teleport and VSAT), power systems (generators, commercial power and battery systems), facilities management, and software/firmware. Programming techniques and examples for simulation of the approaches presented are discussed throughout the book. This powerful resource: Acts as a comprehensive reference and textbook for analysis and design of highly reliable and available telecommunications systems Bridges the fields of system reliability theory, telecommunications system engineering, and computer programming Translates abstract reliability theory concepts into practical tools and techniques for technical managers, engineers and students Provides telecommunication engineers with a holistic understanding of system reliability theory, telecommunications system engineering, and reliability/risk analysis Telecommunications System Reliability Engineering, Theory, and Practice is a must-have guide for telecommunications engineers or engineering students planning to work in the field of telecommunications Telecommunications System Reliability Engineering, Theory, and Practice is a must-have guide for telecommunications engineers or engineering students planning to work in the field of telecommunications.

In the modern symphony of business, each section-from the technical to the managerial-must play in harmony. Authors Ron Itelman and Juan Cruz Viotti introduce a bold methodology to synchronize your business and technical teams, transforming them into a single, high-performing unit. Misalignment between business and technical teams halts innovation. You'll learn how to transcend the root causes of project failure-the ambiguity, knowledge gaps, and blind spots that lead to wasted efforts. The unifying methodology in this book will teach you these alignment tools and more: The four facets of data products: A simple blueprint that encapsulates data and business logic helps eliminate the most common causes of wasted time and misunderstanding The concept compass: An easy way to identify the biggest sources of misalignment Success spectrums: Define the required knowledge and road map your team needs to achieve success JSON Schema: Leverage JSON and JSON Schema to technically implement the strategy at scale, including extending JSON Schema with custom keywords, understanding JSON Schema annotations, and hosting your own schema registry Data hygiene: Learn how to design high-quality datasets aligned with creating real business value, and protect your organization from the most common sources of pain

AVOID THE MISTAKES THAT OTHERS MAKE – LEARN WHAT LEADS TO BEST PRACTICE AND KICKSTART SUCCESS This groundbreaking resource provides comprehensive coverage across all aspects of business analytics, presenting proven management guidelines to drive sustainable differentiation. Through a rich set of case studies, author Evan Stubbs reviews solutions and examples to over twenty common problems spanning managing analytics assets and information, leveraging technology, nurturing skills, and defining processes. Delivering Business Analytics also outlines the Data Scientist’s Code, fifteen principles that when followed ensure constant movement towards effective practice. Practical advice is offered for addressing various analytics issues; the advantages and disadvantages of each issue’s solution; and how these solutions can optimally create organizational value. With an emphasis on real-world examples and pragmatic advice throughout, Delivering Business Analytics provides a reference guide on: The economic principles behind how business analytics leads to competitive differentiation The elements which define best practice The Data Scientist’s Code, fifteen management principles that when followed help teams move towards best practice Practical solutions and frequent missteps to twenty-four common problems across people and process, systems and assets, and data and decision-making Drawing on the successes and failures of countless organizations, author Evan Stubbs provides a densely packed practical reference on how to increase the odds of success in designing business analytics systems and managing teams of data scientists. Uncover what constitutes best practice in business analytics and start achieving it with Delivering Business Analytics.