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Applications of Data Management and Analysis
This book addresses and examines the impacts of applications and services for data management and analysis, such as infrastructure, platforms, software, and business processes, on both academia and industry. The chapters cover effective approaches in dealing with the inherent complexity and increasing demands of big data management from an applications perspective.Various case studies included have been reported by data analysis experts who work closely with their clients in such fields as education, banking, and telecommunications. Understanding how data management has been adapted to these applications will help students, instructors and professionals in the field. Application areas also include the fields of social network analysis, bioinformatics, and the oil and gas industries.
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Handbook of Metrics for Research in Operations Management
The Handbook of Metrics for Research in Operations Management is a compilation of multi-item scales and objective items that can be used to construct surveys and questionnaires in Operations Management. Such surveys can be sent to manufacturing or service managers to collect empirical data on their operations for research purposes. The metrics can be used to measure constructs such as Total Quality Management, Just-In-Time manufacturing, Operations Strategy, Technology Management, Supply Chain Management, performance of operations and related concepts.Key FeaturesEmploys a systematic summary of 1803 multi-item, measurement scales, and objective metrics: Derived from 230 published articles, comprehensive summaries for each scale are available in one source.Cross-classifies measurement scales in two ways: Each scale is classified by categories that are based on OM research topics accepted by major OM journals and by first author names.Provides a discussion of objective measures used in OM research: Illustrative items and examples from 5 questionnaires are given.Increases the efficiency and potential rigor of the researcher′s efforts: Discussion and selected bibliography on key issues for careful construction of new items encourage further development and refinement of psychometrically sound OM metrics.Intended AudienceThis Handbook is an essential, time-saving resource for virtually all operations, supply chain, and technology scholars and practitioners interested in empirical research. A vital addition to all academic libraries, it is intended to serve as a basic reference and guide for construction of surveys and other structured data collection on OM topics.
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HBR's 10 Must Reads on AI, Analytics, and the New Machine Age (with bonus article "Why Every Company Needs an Augmented Reality Strategy" by Michael E. Porter and James E. Heppelmann)
Intelligent machines are revolutionizing business.Machine learning and data analytics are powering a wave of groundbreaking technologies. Is your company ready?If you read nothing else on how intelligent machines are revolutionizing business, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand how these technologies work together, how to adopt them, and why your strategy can't ignore them.In this book you'll learn how:Data science, driven by artificial intelligence and machine learning, is yielding unprecedented business insightsBlockchain has the potential to restructure the economyDrones and driverless vehicles are becoming essential tools3-D printing is making new business models possibleAugmented reality is transforming retail and manufacturingSmart speakers are redefining the rules of marketingHumans and machines are working together to reach new levels of productivityThis collection of articles includes "Artificial Intelligence for the Real World," by Thomas H. Davenport and Rajeev Ronanki; "Stitch Fix's CEO on Selling Personal Style to the Mass Market," by Katrina Lake; "Algorithms Need Managers, Too," by Michael Luca, Jon Kleinberg, and Sendhil Mullainathan; "Marketing in the Age of Alexa," by Niraj Dawar; "Why Every Organization Needs an Augmented Reality Strategy," by Michael E. Porter and James E. Heppelmann; "Drones Go to Work," by Chris Anderson; "The Truth About Blockchain," by Marco Iansiti and Karim R. Lakhani; "The 3-D Printing Playbook," by Richard A. D’Aveni; "Collaborative Intelligence: Humans and AI Are Joining Forces," by H. James Wilson and Paul R. Daugherty; "When Your Boss Wears Metal Pants," by Walter Frick; and "Managing Our Hub Economy," by Marco Iansiti and Karim R. Lakhani.
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Large-Scale Data Analytics with Python and Spark
Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
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Perspectives in Operations Management
In the fall of 1992 a conference honoring Elwood S. Buffa was held at the Anderson Graduate School of Management of the University of California, Los Angeles. This book is a collection of the work presented at that conference. The scholars who gathered to honor El are the prominent researchers in the field of Operations Management. Their collective work published in this book represents the richness of the field and provides the reader with valuable insights into its important issues and problems. While any grouping of the articles by these distinguished scholars will be arbitrary, I have organized the book in four sections. In the first section the articles dealing with the strategic issues in Operations Management are compiled. The articles deal with continuous improvement, quality, services, supply chain management, and creating value through operations. The articles that explore the interface of Operations Management with other functional areas, e.g. engineering and marketing, are grouped in the second section. The third section of the book contains articles that attempt to model some important planning problems that arise in the management of production and operations. Some of the papers in this section provide state of the art reviews of selected topic areas. Finally, the fourth section contains articles that deal with future directions for Operations Management. The authors offer several insights into the future evolution of the field. The book begins with the keynote address given by El Buffa at the start of the conference on November 2, 1991.
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AI Meets Strategy
Explore how Artificial Intelligence (AI) is driving the evolution of product management through a unified approach to data. Written as a practical guide, this book equips modern product managers with the tools to harness AI and data to design, build, and scale innovative products. Through actionable frameworks and real-world examples, you'll learn to define impactful AI problem statements and evaluate financial ROI and feasibility. Beyond strategy, this book addresses data operating model challenges such as data quality, system integration, adoption barriers, ethics, and governance. You'll be introduced to a practical framework for Trustworthy AI, one that combines sound data practices, ethical principles, and responsible governance with real-world product management tools. Whether you are at a startup experimenting with AI or leading transformation in a global enterprise, AI Meets Strategy is your roadmap to building AI-enabled products that people believe in. What You Will Learn Adopt an AI-first mindset, where AI is central to product strategy. Design sustainable systems that balance innovation with societal impact. Craft enterprise-wide AI strategies that align with business goals, governance standards, and ethical principles Who This Book is For Aspiring to senior Product Managers
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Operations Management for Service Industries
Effective service delivery requires a transition from high-volume, long-run operations to low-volume, short-run output. In conventional terms, inefficiency is inherent in such a shift. The Japanese experience suggests, however, that this convention is in error; success is available if we can only organize operations away from capital toward labor intensiveness with emphasis on multipurpose machinery and multiskilled workers as the foundation. Wholly new devices that accept the inevitability of bottlenecks and focus on managing them are required for managing work flow. A century of mass production has set in place habits and concepts of operations management that are inappropriate to the need. A new vision is needed. This book outlines an operations vision based on proven principles of management and organization science that can guide the way into an emerging service era.Glenn Bassett looks at a variety of service industries from the perspective of cost and quality management. He argues the basic inevitablitity of suboptimized plant and equipment utilization. The potential for conflict between commodity and noncommodity dimensions of service is examined. Basic methods of cost control and work flow management are described. The varied and sometimes shifting bases of service quality are described in considerable detail, industry by industry where necessary. Methods for selecting and training effective service-providers are reviewed. Reform of government service as metaphor and model for the service revolution is detailed. The focus always is on sound, cost-effective, high-quality service delivery using the best available operations methods. It is sound operations management that will contribute genuine value to tomorrow's service industries. The basics of that discipline are the subject of this book.
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Data Analytics Toolkit
Expand your data analysis toolkit with Python, R, and Tableau Data Analytics Common Core is designed to help professionals transition from spreadsheet-based analysis to more powerful tools like Python, R, and Tableau. With spreadsheets being the foundation of modern data analysis, this book bridges the gap for those without innate programming skills, offering a practical guide to enhancing your data analysis toolkit. Through a series of real-world case studies, the book demonstrates how to create the same graphics using Excel, Tableau, Python, and R. By starting with Excel and gradually moving to more advanced tools, readers will learn the strengths and limitations of each platform. This comparative approach not only teaches how to use these tools but also deepens understanding of core data analysis concepts, making it easier to choose the right tool for each task. Ideal for both beginners and those looking to expand their existing skills, Data Analytics Common Core provides step-by-step instructions, including software installation and file management, to ensure a smooth transition. Whether you're just starting out or looking to enhance your data visualization and analysis capabilities, this book is a valuable resource for mastering Python, R, and Tableau.
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Geometrical Enhancements Based on Data-Driven Design Decisions
The text presents various design and modelling solutions for effective decision-making that are grounded on the basics of data analytics. It further discusses important topics such as sustainable design and data-driven design synthesis, product analytics and its role in sustainable development, and descriptive models for the geometrical evaluation of product features.This book:Familiarize the readers with the numerous decision protocols that can be utilized for deducing geometrical qualities using data-driven design analysisOffers a more comprehensive approach to multidisciplinary engineering applications using data-driven modelling and analysisDiscusses multi-objective optimization for product design and development through data-driven decision-makingExplains information processing and analysis through data visualization and data miningCovers artificial intelligence and machine learning-based decision models and their effective utilization across interdisciplinary applicationsIt is primarily written for senior undergraduate, graduate students, and academic researchers in fields including industrial engineering, production engineering, manufacturing engineering, mechanical engineering, and aerospace engineering.
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Bioinformatics Tools and Big Data Analytics for Patient Care
Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers. For this reason, bioinformatics tools perform a vital role in extracting and analyzing information from databases. Bioinformatics Tools and Big Data Analytics for Patient describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery for patient care. The book gives details about the recent developments in the fields of artificial intelligence, cloud computing, and data analytics. It highlights the advances in computational techniques used to perform intelligent medical tasks.Features:Presents recent developments in the fields of artificial intelligence, cloud computing, and data analytics for improved patient care.Describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery.Summarizes several strategies, analyses, and optimization methods for patient healthcare.Focuses on drug discovery and development by cloud computing and data-driven researchThe targeted audience comprises academics, research scholars, healthcare professionals, hospital managers, pharmaceutical chemists, the biomedical industry, software engineers, and IT professionals.
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Applied Data Analytics
The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors. Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts. This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.
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Lean, Smart, Digital Supply Chain
Technology plays a key role in enabling lean and agile supply chain operations. For example, connecting to suppliers in real-time facilitates re-supplying parts and materials for a just-in-time production environment. But choosing the wrong technology can create waste in terms of the time, effort, and money spent evaluating, selecting, implementing, and using it.Furthermore, lean has been traditionally thought of as a "pen and pencil" technique as they were mostly confined to a single facility. As a consequence, while there are many books written on lean manufacturing, lean office, and, to a lesser degree, lean global supply chain, most if not all barely discuss the role and impact of technology in process improvement, and there aren’t many books that combine the topics of a lean and agile supply chain and technology (smart and otherwise) in this way.This book makes the case that technology is a key enabler of a lean supply chain and is unique in that it links lean and agile thinking with available and affordable technologies to get the most out of improved processes.Essentially, this book details various supply chain and logistics management areas where lean and agile thinking in combination with existing and emerging technologies such as the Internet, e-commerce, Enterprise Resource Planning (ERP) systems, Robotics, IoT, AI, and Data Analytics can take an organization to the next level through increased speed, accuracy, integration, and collaboration among all parties in the supply chain.
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Modelling and Performance Analysis of Cyclic Systems
Striking a balance between state-of-the-art research and practical applications, this book provides a forum for contributions that cover the main research challenges in the cyclic modeling, development, and validation of concurrently acting distributed production systems: systems that employ multi-assortment production in large quantities, are characterized by gradual changes in their product mix, and exclusively manufacture products in a cyclic manner. Similar issues also arise in computer systems, e.g., embedded systems. Cyclic optimization problems that occur in them are unique and under-researched, but are attracting new interest primarily due to their great practical importance and the difficulty involved in obtaining efficient algorithms for solving specific cases with real constraints arising from manufacturing practice. Addressing these and other topics, the book will be of great interest to researchers in computer science, operations management, and production control, as well as practicing managers and engineers.
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Cyberspace, Data Analytics, and Policing
Cyberspace is changing the face of crime. For criminals it has become a place for rich collaboration and learning, not just within one country; and a place where new kinds of crimes can be carried out, and a vehicle for committing conventional crimes with unprecedented range, scale, and speed. Law enforcement faces a challenge in keeping up and dealing with this new environment. The news is not all bad – collecting and analyzing data about criminals and their activities can provide new levels of insight into what they are doing and how they are doing it. However, using data analytics requires a change of process and new skills that (so far) many law enforcement organizations have had difficulty leveraging. Cyberspace, Data Analytics, and Policing surveys the changes that cyberspace has brought to criminality and to policing with enough technical content to expose the issues and suggest ways in which law enforcement organizations can adapt.Key Features:Provides a non-technical but robust overview of how cyberspace enables new kinds of crime and changes existing crimes.Describes how criminals exploit the ability to communicate globally to learn, form groups, and acquire cybertools.Describes how law enforcement can use the ability to collect data and apply analytics to better protect society and to discover and prosecute criminals.Provides examples from open-source data of how hot spot and intelligence-led policing can benefit law enforcement.Describes how law enforcement can exploit the ability to communicate globally to collaborate in dealing with trans-national crime.
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Application of Advanced Optimization Techniques for Healthcare Analytics
Application of Advanced Optimization Techniques for Healthcare Analytics, 1st Edition, is an excellent compilation of current and advanced optimization techniques which can readily be applied to solve different hospital management problems. The healthcare system is currently a topic of significant investigation to make life easier for those who are disabled, old, or sick, as well as for young children. The emphasis of the healthcare system has evolved throughout time due to several emerging beneficial technologies, such as personal digital assistants (PDAs), data mining, the internet of things, metaheuristics, fog computing, and cloud computing.Metaheuristics are strong technology for tackling several optimization problems in various fields, especially healthcare systems. The primary advantage of metaheuristic algorithms is their ability to find a better solution to a healthcare problem and their ability to consume as little time as possible. In addition, metaheuristics are more flexible compared to several other optimization techniques. These algorithms are not related to a specific optimization problem but could be applied to any optimization problem by making some small adaptations to become suitable to tackle it.The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable optimization approach when making decisions to schedule patients under crowding environments with minimized human errors.
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Manufacturing Operations Management
The main purpose of this book is to introduce the essential theories and tools for production (manufacturing operations) management for students in engineering, junior professionals in supply chain and production managers who are starting their career in a manufacturing firm.With a focus on selected key techniques and a practical application of these skills, the book uses a real-world inspired case studies while providing readers with in-depth exploration. Designed as a coaching handbook for instructors or motivated self-learners, it is an ideal resource for project-based learning.It is suitable for graduates in technology management and engineering, and professionals in the field of manufacturing who want to revise their practical knowledge while enhancing theoretical background.
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Data Science and Big Data Analytics
This book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing. It discusses major issues pertaining to big data analysis using computational intelligence techniques, and the conjectural elements are supported by simulation and modelling applications to help address real-world problems. An extensive bibliography is provided at the end of each chapter. Further, the main content is supplemented by a wealth of figures, graphs, and tables, offering a valuable guide for researchers in the field of big data analytics and computational intelligence.
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Big Data Analytics in Astronomy, Science, and Engineering
This book constitutes the proceedings of the 11th International Conference on Big Data Analytics in Astronomy, Science, and Engineering, BDA 2023, which took place in Aizu, Japan during December 5–7, 2023.The 19 full papers included in this book were carefully reviewed and selected from 55 submissions. They were organized in topical sections as follows: Data management and visualization; data science: architectures and systems; data science and applications; and cyber systems and information security.
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Big Data Analytics and Knowledge Discovery
This book constitutes the proceedings of the 25th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2023, which took place in Penang, Malaysia, during August 29-30, 2023. The 18 full papers presented together with 19 short papers were carefully reviewed and selected from a total of 155 submissions.They were organized in topical sections as follows: Data quality; advanced analytics and pattern discovery; machine learning; deep learning; and data management.
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Highway Safety Analytics and Modeling
Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety data analysis in a single. reference. The book includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating analysis results. It discusses the challenges of working with crash and naturalistic data, identifies problems and proposes well-researched methods to solve them. Finally, the book examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes.
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Modelling and Simulation in Management Sciences
This book includes a collection of selected papers presented at the International Conference on Modelling and Simulation in Engineering, Economics, and Management, held at the Faculty of Economics and Business at the University of Girona, Spain, 28-29 June 2018.The conference was organized by the Association for the Advancement of Modelling and Simulation Techniques in Enterprises (AMSE) and the University of Girona with the aim of promoting research in the field of modelling, simulation and management science. This book presents original research studies related to fuzzy logic, soft computing and uncertainty, as well as a number of papers in the field of bibliometrics in social sciences. Presenting new advances in these areas, with a special focus on management, economics and social sciences.It is of great interest to researchers and Ph.D. students working in the field of fuzzy logic, soft computing, uncertainty and bibliometrics.
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Data Analytics for IT Networks
Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data
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Data Analytics Applications in Latin America and Emerging Economies
This book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors. Using cases from Latin America and other emerging economies, it examines analytics knowledge applications where a solution has been achieved. Written for business students and professionals as well as researchers, the book is filled with practical insight into applying concepts and implementing processes and solutions. The eleven case studies presented in the book incorporate the whole analytics process and are useful reference examples for applying the analytics process for SME organizations in both developing and developed economies. The cases also identify multiple tacit factors to deal with during the implementation of analytics knowledge management processes. These factors, which include data cleaning, data gathering, and interpretation of results, are not always easily identified by analytics practitioners.This book promotes the understanding of analytics methods and techniques. It guides readers through numerous techniques and methods available to analytics practitioners by explaining the strengths and weaknesses of these methods and techniques.
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Data Science and Big Data Analytics
This book features high-quality research papers presented at the Fifth International Conference on Data Science and Big Data Analytics (IDBA 2025), organized by Symbiosis University of Applied Sciences, Indore, India, in association with ACM and IEEE Computer Society in hybrid mode during June 27–28, 2025. This book discusses topics such as data science, artificial intelligence, machine learning, quantum computing, big data and cloud security, computation security, big data security, information security, forecasting, data analytics, mathematics for data science, graph theory and application in data science, data visualization, computer vision, and analytics for social networks.
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Supply Chain Strategy, Second Edition: Unleash the Power of Business Integration to Maximize Financial, Service, and Operations Performance
The industry standard in supply chain management—fully revised and updated to provide today’s logistics solutionsThe proven pillars of success in logistics and supply chain management introduced in the first edition of Supply Chain Strategy now guide the supply chains of many of the world’s most successful organizations, including 3M, Abbott, BP, Coca-Cola, Disney, Hallmark, Honda, Mitsubishi, Oxxo-FEMSA, Payless, P&G, Pratt & Whitney, Wal-Mart, Rio Tinto, and many others.This Second Edition features up-to-date case studies showing how those companies and more meet supply chain goals and helps you overcome your own challenges with the latest supply chain innovations, including big-data analytics, supply chain command and control centers, large-scale supply chain optimization, integrated supply chain planning, real-time global supply chain visibility, omni-channel logistics, re-shoring, global-sourcing optimization, cloud-based supply chain management, supply chain finance, global trade management, and fourth-party logistics.
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Big Data and Learning Analytics in Higher Education
This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns.
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Data-Enabled Analytics
This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
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Intelligent Data-Analytics for Condition Monitoring
Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications looks at intelligent and meaningful uses of data required for an optimized, efficient engineering processes. In addition, the book provides application perspectives of various deep learning models for the condition monitoring of electrical equipment. With chapters discussing the fundamentals of machine learning and data analytics, the book is divided into two parts, including i) The application of intelligent data analytics in Solar PV fault diagnostics, transformer health monitoring and faults diagnostics, and induction motor faults and ii) Forecasting issues using data analytics which looks at global solar radiation forecasting, wind data forecasting, and more. This reference is useful for all engineers and researchers who need preliminary knowledge on data analytics fundamentals and the working methodologies and architecture of smart grid systems.
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Tourism Analytics Before and After COVID-19
This book is compilation of different analytics and machine learning techniques focusing on the tourism industry, particularly in measuring the impact of COVID-19 as well as forging a path ahead toward recovery. It includes case studies on COVID-19's effects on tourism in Europe, Hong Kong, China, and Singapore with the objective of looking at the issues through a data analytical lens and uncovering potential solutions. It adopts descriptive analytics, predictive analytics, machine learning predictive models, and some simulation models to provide holistic understanding.There are three ways in which readers will benefit from reading this work. Firstly, readers gain an insightful understanding of how tourism is impacted by different factors, its intermingled relationship with macro and business data, and how different analytics approaches can be used to visualize the issues, scenarios, and resolutions. Secondly, readers learn to pick up data analytics skills from the illustrated examples. Thirdly, readers learn the basics of Python programming to work with the different kinds of datasets that may be applicable to the tourism industry.
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Strategic System Assurance and Business Analytics
This book systematically examines and quantifies industrial problems by assessing the complexity and safety of large systems. It includes chapters on system performance management, software reliability assessment, testing, quality management, analysis using soft computing techniques, management analytics, and business analytics, with a clear focus on exploring real-world business issues. Through contributions from researchers working in the area of performance, management, and business analytics, it explores the development of new methods and approaches to improve business by gaining knowledge from bulk data. With system performance analytics, companies are now able to drive performance and provide actionable insights for each level and for every role using key indicators, generate mobile-enabled scorecards, time series-based analysis using charts, and dashboards.In the current dynamic environment, a viable tool known as multi-criteria decision analysis (MCDA) is increasingly being adopted to deal with complex business decisions. MCDA is an important decision support tool for analyzing goals and providing optimal solutions and alternatives. It comprises several distinct techniques, which are implemented by specialized decision-making packages. This book addresses a number of important MCDA methods, such as DEMATEL, TOPSIS, AHP, MAUT, and Intuitionistic Fuzzy MCDM, which make it possible to derive maximum utility in the area of analytics. As such, it is a valuable resource for researchers and academicians, as well as practitioners and business experts.
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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.
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Big Data Analytics and Its Impact on Basin Water Agreements and International Water Law
Big Data Analytics and Its Impact on Basin Water Agreements and International Water Law represents the state of the art when it comes to the use of disruptive technologies in the transboundary water context and its impact on international water law. Indeed, the case study provided in this manuscript which represents the most relevant example where big data is being used in the transboundary water context highlights this reality. The readers will understand current and also future potential impact of big data on water resources in the general context of disruptive technologies.
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Resource Management and Performance Analysis of Wireless Communication Networks
With the diversification of Internet services and the increase in mobile users, efficient management of network resources has become an extremely important issue in the field of wireless communication networks (WCNs). Adaptive resource management is an effective tool for improving the economic efficiency of WCN systems as well as network design and construction, especially in view of the surge in mobile device demands.This book presents modelling methods based on queueing theory and Markov processes for a wide variety of WCN systems, as well as precise and approximate analytical solution methods for the numerical evaluation of the system performance.This is the first book to provide an overview of the numerical analyses that can be gleaned by applying queueing theory, traffic theory and other analytical methods to various WCN systems. It also discusses the recent advances in the resource management of WCNs, such as broadband wireless access networks, cognitive radio networks, and green cloud computing. It assumes a basic understanding of computer networks and queueing theory, and familiarity with stochastic processes is also recommended.The analysis methods presented in this book are useful for first-year-graduate or senior computer science and communication engineering students. Providing information on network design and management, performance evaluation, queueing theory, game theory, intelligent optimization, and operations research for researchers and engineers, the book is also a valuable reference resource for students, analysts, managers and anyone in the industry interested in WCN system modelling, performance analysis and numerical evaluation.
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Fundamentals of Data Analytics
This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.
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Big Data Analytics in Healthcare
This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms.
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Algorithmic Aspects in Information and Management
This volume constitutes the proceedings of the 12th International Conference on Algorithmic Aspects in Information and Management, AAIM 2018, held in Dallas, Texas, USA, in December 2018.The 25 full papers presented were carefully reviewed and selected. The papers deal with current trends of research on algorithms, data structures, operation research, combinatorial optimization and their applications.
R 2,178.46
Next Generation Optical Network Design and Modelling
Optical networks are leaving the labs and becoming a reality. Despite the current crisis of the telecom industry, our everyday life increasingly depends on communication networks for information exchange, medicine, education, data transfer, commerce, and many other endeavours. High capacity links are required by the large futemet traffic demand, and optical networks remain one of the most promising technologies for meeting these needs. WDM systems are today widely deployed, thanks to low-cost at extreme data rates and high reliability of optical components, such as optical amplifiers and fixed/tunable filters and transceivers. Access and metropolitan area networks are increasingly based on optical technologies to overcome the electronic bottleneck at the network edge. Traditional multi-layer architectures, such as the widely deployed IP/ATM/SDH protocol stack, are increasingly based on WDM transport; further efforts are sought to move at the optical layer more of the functionalities available today in higher protocol layers. New components and subsystems for very high speed optical networks offer new design opportunities to network operators and designers. The trends towards dynamically configurable all-optical network infrastructures open up a wide range of new network engineering and design choices, which must face issues such as interoperability and unified control and management.
R 3,071.10
