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Advanced Operations Management for Complex Systems Analysis
This book focuses on operations management methods for analysing complex systems from a system engineering perspective. It presents various advanced multi-criteria decision analysis methods for investigating factors that influence complex systems. In turn, it shows how to improve systems’ performance, including their competitiveness, safety, and sustainability. The book also draws on examples of typical virtual systems such as tourism, aviation maintenance, and waste-to-wealth systems to illustrate the operations management methods discussed. Cases from day-to-day life are used to elicit heuristic questions on the operations management methods presented in each chapter. The book will help researchers, operations managers, and engineers alike to understand the latest advances in operations management methods for analysing complex systems from the standpoint of system engineering.
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Supply Chain and Operations Management
This textbook presents a structured exploration of the integral processes and strategies that underpin effective supply chain and operations management. Emphasizing practical knowledge, it delves into the fundamental components of sourcing, operations, and logistics, highlighting their interconnected roles in delivering goods and services efficiently. The revised first edition features minor corrections throughout.
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Big Data Analytics
Big Data Analytics: Digital Marketing and Decision-Making covers the advances related to marketing and business analytics. Investment marketing analytics can create value through proper allocation of resources and resource orchestration processes. The use of data analytics tools can be used to improve and speed decision-making processes.Chapters examining analytics for decision-making cover such topics as: Big data analytics for gathering business intelligenceData analytics and consumer behaviorThe role of big data analytics in organizational decision-makingThis book also looks at digital marketing and focuses on such areas as: The prediction of marketing by consumer analyticsWeb analytics for digital marketingSmart retailingLeveraging web analytics for optimizing digital marketing strategiesBig Data Analytics: Digital Marketing and Decision-Making aims to help organizations increase their profits by making better decisions on time through the use of data analytics. It is written for students, practitioners, industry professionals, researchers, and faculty working in the field of commerce and marketing, big data analytics, and organizational decision-making.
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Data Management and Analysis
Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
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Applied Analytics through Case Studies Using SAS and R
Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. What You'll Learn Understand analytics and basic data concepts Use an analytical approach to solve Industrial business problems Build predictive model with machine learning techniquesCreate and apply analytical strategiesWho This Book Is ForData scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling.
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Modelling Trends in Solid and Hazardous Waste Management
This book explores state-of-art techniques based on methodological and modeling aspects of solid and hazardous waste management, specifically focusing on the recent trends in data acquisition and robust modeling of the results obtained. In addition to an in-depth description of the recent regulatory paradigm for solid waste disposal and revealing insights into solid waste management models, the book also addresses significant case history and remediation methodologies for sustainable development in emerging economies like India, China and Brazil.The main emphasis is on a suitable regulatory framework with site-specific baseline calibration and aimed at the robust modeling of contaminant transport and its remediation. This is based on instructive case history in various locations/regions worldwide. The focus on recent modeling and quantification methods is the backbone of the book. One of the major aspects discussed is the application of non-invasive methods for studies related to the Earth's interior, which are increasingly preferred over invasive techniques thanks to their economic utility, as well as robust techniques for the interpretation of geophysical data. The increasing demand for groundwater and energy resources, especially for rapidly emerging countries with large populations like India and China, has made it vital to derive safe utilization approaches for our resources, including suitable waste disposal and remediation methodologies that can be adopted for 'contaminated sites.'
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Healthcare Operations Management
Addressing the entire care chain, this book presents the outcomes of advanced research on healthcare operations management based on real-world data and practices in China. It includes hands-on methods and applications in this interdisciplinary research field, which combines healthcare service, operations management, industrial engineering and information technology.The content is divided into three parts, reflecting the entire care chain. The first part discusses the pre-hospital service stage and explores resource deployment problems in emergency medical service, such as ambulance allocation. The second part focuses on inpatient care services, including staffing and task allocation among nurses and doctors based on multi-project management under uncertainties. In addition, a highly promising diagnosis approach is proposed and a specific algorithm is derived on the basis of real-world datasets which can improve the diagnosis accuracy remarkably. In turn, the third part considers the post-hospital service stage, which most often takes place at community hospitals, and provides a quantitative evaluation and optimization of scheduling for tasks and team members for home care services.The book is intended for a broad audience, including students, researchers and practitioners working in various areas of healthcare management, service management, and operations management.
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Operations Research and Big Data
The development of Operations Research (OR) requires constant improvements, such as the integration of research results with business applications and innovative educational practice. The full deployment and commercial exploitation of goods and services generally need the construction of strong synergies between educational institutions and businesses. The IO2015 -XVII Congress of APDIO aims at strengthening the knowledge triangle in education, research and innovation, in order to maximize the contribution of OR for sustainable growth, the promoting of a knowledge-based economy, and the smart use of finite resources. The IO2015-XVII Congress of APDIO is a privileged meeting point for the promotion and dissemination of OR and related disciplines, through the exchange of ideas among teachers, researchers, students , and professionals with different background, but all sharing a common desire that is the development of OR.
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Advances in Big Data Analytics
Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence.Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.
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Decision Making Optimization Models for Business Partnerships
Efficiency and productivity improvement are imperative for businesses to remain competitive in an increasingly dynamic marketplace. While business organizations have the potential to thrive independently, collaborating with others fosters a collective strength that can lead to greater innovation, expanded reach, and shared success.Decision making optimization models for business partnerships are essential, as businesses seldom have all the resources they need, and thus, they require alliances and partnerships with others to enable them to meet their goals. Decision Making Optimization Models for Business Partnerships extends non-parametric data envelopment analysis (DEA) and parametric econometrics approaches to better understand how economic efficiency and market competitiveness are achieved for different types of partnerships and strategic alliances.FeaturesGlobal contributions for a wide range of professionals and academicsInvaluable resources for businesses, analysts, and academics interested in DEA, optimization, and operations research more widelyIntroduces readers to novel approaches, models, and decision making techniques on performance evaluation and business partnerships via the medium of parametric and nonparametric optimization.
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Talent Disruption
Talent Disruption proposes a solution to the human capital challenges faced by service industries amidst unprecedented labor shortages and technological and geopolitical shifts as well as offering a framework for diagnosing the root causes of talent disruption. Talent Disruption provides a detailed roadmap and tools for building a talent engine powered by AI, big data, and analytics. The results are a growing talent pipeline, greater productivity, and higher profit margins.
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Intelligent Customer Complaint Management System with Application to the Transport and Logistics Industry
This thesis addresses the issue of customer complaints in the context of Customer Relationship Management (CRM). After a comprehensive survey of the current literature on CRM, the thesis describes the development of a new intelligent CRM (I-CRM) framework, which integrates text analytics, type mapping, SPSS, structural equation modeling, and linear and fuzzy approaches. This new methodology, in contrast to previous ones, is able to handle customer complaints with respect to different variables, thus allowing organizations to find their key customers and key complaints, and to address and provide solution to the major complaints of the key customers, hence promoting business development. The thesis also describes the successful application of the method to a real-world case, represented by the immeasurable truck drivers complaints at the Fremantle port in Western Australia.
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Industrial Engineering and Operations Management
This proceedings gathers selected, peer-reviewed papers presented at the XXX International Joint Conference on Industrial Engineering and Operations Management (IJCIEOM), held from June 26 to 28, 2024, in Salvador, Brazil. The works in this volume explores critical areas such as Supply Chain risk models, last-mile delivery optimization, stochastic inventory models, and human development focusing on digital training for operations management in emergencies.Tailored to benefit academics, the volume comprises studies predominantly rooted in real-world case studies, systematic, and meta-reviews, offering valuable insights also for professionals within the industrial sector by presenting solutions to intricate industrial challenges.
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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.
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Business Intelligence, Computational Mathematics, and Data Analytics
This book constitutes the proceedings of the Second International Conference on Business Intelligence, Computational Mathematics, and Data Analytics, IBCD 2025, held in Pune, India, during September 26–27, 2025.The 36 full papers and 2 short papers presented in this volume were carefully reviewed and selected from 343 submissions. These papers discuss the latest advancements and innovations in the fields of business intelligence, computational mathematics, and data analytics.
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Consumer-Driven Demand and Operations Management Models
To compete in today's volatile market with rapidly changing consumer tastes and erce competition, companies in the manufacturing and service industries are - ploying new mechanisms to increase sales, market shares, and pro ts. As an - fective mechanism to segment a market comprising of consumers with different needs, preferences, and willingness-to-pay, many rms have used product (or s- vice) variety with different price points to serve different segments of the market, see Ho (1998). Ideally, the price of each of these products (or services) targets a particular segment of customers. For example, airlines often use different terms of sales (refundable/non-refundable, upgradable/non-upgradable, direct/connecting ight, etc. ) to sell economy class tickets at different prices. Likewise, retailers - ten sell the same product at different prices in different channels (company's own web site, dealers' web sites, or company's physical stores) or at different times (- fore, during, and after the selling season), see Talluri and van Ryzin (2005). Ample academic literature in Operations Management and other areas considered these strategies.However, as consumers become more knowledgeable about the product, pricing, organizational and operational policies that the companies deploy for pr- ucts and services, their purchasing begins to change dramatically. In the academic Operations Management literature, consumer demand is often assumedtobe exogenous so that demand functions are usually modeled as well de- ned and exogenously speci ed functions of price and/or other product attributes such as quality.
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Business Intelligence, Computational Mathematics, and Data Analytics
This book constitutes the proceedings of the First International Conference on Business Intelligence, Computational Mathematics, and Data Analytics, IBCD 2024, held in Indore, India, during August 18–19, 2024.The conference focus on applied intelligence across data science, mathematics, healthcare, cybersecurity, and business analytics, and will serve as a driving force for transforming theoretical breakthroughs into practical, real-world solutions with meaningful societal impact.
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Creating Value with Data Analytics in Marketing
This book is a refreshingly practical yet theoretically sound roadmap to leveraging data analytics and data science. The vast amount of data generated about us and our world is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organizations to leverage the information to create value in marketing.Creating Value with Data Analytics in Marketing provides a nuanced view of big data developments and data science, arguing that big data is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. The second edition of this bestselling text has been fully updated in line with developments in the field and includes a selection of new, international cases and examples, exercises, techniques and methodologies.Tying data and analytics to specific goals and processes for implementation makes this essential reading for advanced undergraduate and postgraduate students and specialists of data analytics, marketing research, marketing management and customer relationship management.Online resources include chapter-by-chapter lecture slides and data sets and corresponding R code for selected chapters.
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Data Analytics for Internal Auditors
There are many webinars and training courses on Data Analytics for Internal Auditors, but no handbook written from the practitioner’s viewpoint covering not only the need and the theory, but a practical hands-on approach to conducting Data Analytics. The spread of IT systems makes it necessary that auditors as well as management have the ability to examine high volumes of data and transactions to determine patterns and trends. The increasing need to continuously monitor and audit IT systems has created an imperative for the effective use of appropriate data mining tools. This book takes an auditor from a zero base to an ability to professionally analyze corporate data seeking anomalies.
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Simulation of Recreational Use for Park and Wilderness Management
First Published in 2011. This book describes the application of an advanced analytical technique, simulation modeling, (WUSM) to a significant problem in resources management. It includes ideas which have grown out of practical resource management problems that have progressed through conceptual models to operational tools and finally to application in actual public land management settings. It is similarly rewarding to see the work being adapted for use by the National Park Service and other agencies at home and abroad.
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Knowledge Discovery And Data Design Innovation - Proceedings Of The International Conference On Knowledge Management (Ickm 2017)
To navigate the complex ecosystem of societal challenges, the International Conference on Knowledge Management Conference (ICKM 2017) focused on big data and data analytics as part of the relationship to the wider concept of knowledge management processes and practices.This book includes top papers presenting the major, and diverse, topics discussed at the conference. The papers covered various aspects of big data ranging from enhancing access to the big data to facilitating its wide applications in healthcare, social media, library and information centers, governments, and corporations.
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General Introduction to Data Analytics
A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniquesA unique illustrative example that extends throughout all the chaptersExercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.
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Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics
In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity. Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges.This book is useful for research scholars and students involved in critical condition analysis and computation models.
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World of Business with Data and Analytics
This book covers research work spanning the breadth of ventures, a variety of challenges and the finest of techniques used to address data and analytics, by subject matter experts from the business world. The content of this book highlights the real-life business problems that are relevant to any industry and technology environment. This book helps us become a contributor to and accelerator of artificial intelligence, data science and analytics, deploy a structured life-cycle approach to data related issues, apply appropriate analytical tools & techniques to analyze data and deliver solutions with a difference. It also brings out the story-telling element in a compelling fashion using data and analytics. This prepares the readers to drive quantitative and qualitative outcomes and apply this mindset to various business actions in different domains such as energy, manufacturing, health care, BFSI, security, etc.
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Demystifying Big Data Analytics for Industries and Smart Societies
This book aims to provide readers with a comprehensive guide to the fundamentals of big data analytics and its applications in various industries and smart societies. What sets this book apart is its in-depth coverage of different aspects of big data analytics, including machine learning algorithms, spatial data analytics, and IoT-based smart systems for precision agriculture. The book also delves into the use of big data analytics in healthcare, energy management, and agricultural development, among others. The authors have used clear and concise language, along with relevant examples and case studies, to help readers understand the complex concepts involved in big data analytics.Key Features:Comprehensive coverage of the fundamentals of big data analyticsIn-depth discussion of different aspects of big data analytics, including machine learning algorithms, spatial data analytics, and IoT-based smart systemsPractical examples and case studies to help readers understand complex conceptsCoverage of the use of big data analytics in various industries, including healthcare, energy management, and agricultureDiscussion of challenges and legal frameworks involved in big data analyticsClear and concise language that is easy to understandThis book is a valuable resource for business owners, data analysts, students, and anyone interested in the field of big data analytics. It provides readers with the tools they need to leverage the power of big data and make informed decisions that can help their organizations succeed. Whether you are new to the field or an experienced practitioner, "Demystifying Big Data Analytics for Industries and Smart Societies" is must-read.
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Exam Ref DP-600 Implementing Analytics Solutions Using Microsoft Fabric
Prepare for Microsoft Exam DP-600 and demonstrate your real-world mastery of Microsoft Fabric analytics solution development. Designed for working Fabric analytics engineers, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified Solutions Associate level. Focus on the expertise measured by these objectives: Plan, implement, and manage a solution for data analyticsPrepare and serve dataImplement and manage semantic modelsExplore and analyze data This Microsoft Exam Ref: Organizes its coverage by exam objectivesFeatures strategic, what-if scenarios to challenge youAssumes you want to validate your ability to design and build diverse Microsoft Fabric analytics solutions About the Exam Exam DP-600 focuses on knowledge needed to implement and plan a data analytics environment; manage an analytics development lifecycle; create objects in a lakehouse or warehouse; copy data, transform data, and optimize performance; implement, design, and build semantic models; optimize enterprise-scale semantic models; perform exploratory analytics; query data by using SQL. About Microsoft Certification Passing this exam fulfills your requirements for Microsoft Certified: Fabric Analytics Engineer Associate credential, demonstrating your readiness to design, build, test, and maintain Microsoft Fabric data analytics solutions, and partner with other professionals and clients to implement them. See full details at: microsoft.com/learn
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Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021)
This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.
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Implementing Operations Management Suite
Learn how to protect, back up, recover, and monitor your data and infrastructure in the cloud with Microsoft's Operations Management Suite (OMS), Azure Backup, and Azure Site Recovery.Implementing Operations Management Suite starts with an overview of the Operations Management Suite, followed by an introduction to Azure virtual machines and virtual networks. Chapters cover Azure Backup and how to configure it, followed by deep dives into aspects of Azure Site Recovery (ASR): how it works, how to configure it, how to streamline your disaster recovery failover from on-premises to Azure, and so on. Learn about protection groups, how to perform planned and unplanned failover, and more. Windows IT pro consultant, trainer and MVP Peter De Tender takes you through the necessary theory and background on each topic along with hands-on step-by-step lab guides to help you implement and configure each feature for yourself. You’ll also find out how to estimate your platform costs when using Azure infrastructure components, making this book your one-stop guide to the latest disaster recovery services in Microsoft Azure. What You'll LearnUnderstand current concepts and challenges in IT disaster recoveryGet introduced to Microsoft Azure, Azure virtual networks and Azure virtual machinesProtect your data in the cloud with Azure Backup, and the configuration options availableUnderstand how to protect, recover, and monitor your environment with Azure Site Recovery Manager, and the configuration options availableExtend Azure Site Recovery Manager to non-Hyper-V workloadsWho This Book Is ForIT professionals and IT decision makers who are interested in learning about Azure backup and Azure Site Recovery Manager in order to build and/or optimize their IT disaster recovery scenarios.
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Big Data Analytics Strategies for the Smart Grid
By implementing a comprehensive data analytics program, utility companies can meet the continually evolving challenges of modern grids that are operationally efficient, while reconciling the demands of greenhouse gas legislation and establishing a meaningful return on investment from smart grid deployments.Readable and accessible, Big Data Analytics Strategies for the Smart Grid addresses the needs of applying big data technologies and approaches, including Big Data cybersecurity, to the critical infrastructure that makes up the electrical utility grid. It supplies industry stakeholders with an in-depth understanding of the engineering, business, and customer domains within the power delivery market.The book explores the unique needs of electrical utility grids, including operational technology, IT, storage, processing, and how to transform grid assets for the benefit of both the utility business and energy consumers. It not only provides specific examples that illustrate how analytics work and how they are best applied, but also describes how to avoid potential problems and pitfalls.Discussing security and data privacy, it explores the role of the utility in protecting their customers’ right to privacy while still engaging in forward-looking business practices. The book includes discussions of: SAS for asset management toolsThe AutoGrid approach to commercial analyticsSpace-Time Insight’s work at the California ISO (CAISO)This book is an ideal resource for mid- to upper-level utility executives who need to understand the business value of smart grid data analytics. It explains critical concepts in a manner that will better position executives to make the right decisions about building their analytics programs.At the same time, the book provides sufficient technical depth that it is useful for data analytics professionals who need to better understand the nuances of the engineering and business challenges unique to the utilities industry.
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Optimal Operation and Control of Power Systems Using an Algebraic Modelling Language
This book presents mathematical models of demand-side management programs, together with operational and control problems for power and renewable energy systems. It reflects the need for optimal operation and control of today’s electricity grid at both the supply and demand spectrum of the grid. This need is further compounded by the advent of smart grids, which has led to increased customer/consumer participation in power and renewable energy system operations. The book begins by giving an overview of power and renewable energy systems, demand-side management programs and algebraic modeling languages. The overview includes detailed consideration of appliance scheduling algorithms, price elasticity matrices and demand response incentives. Furthermore, the book presents various power system operational and control mathematical formulations, incorporating demand-side management programs. The mathematical formulations developed are modeled and solved using the Advanced Interactive Multidimensional Modeling System (AIMMS) software, which offers a powerful yet simple algebraic modeling language for solving optimization problems. The book is extremely useful for all power system operators and planners who are concerned with optimal operational procedures for managing today’s complex grids, a context in which customers are active participants and can curb/control their demand. The book details how AIMMS can be a useful tool in optimizing power grids and also offers a valuable research aid for students and academics alike.
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Adoption of Data Analytics in Higher Education Learning and Teaching
The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms.This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.
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Fundamentals of Spatial Analysis and Modelling
This textbook provides comprehensive and in-depth explanations of all topics related to spatial analysis and spatiotemporal simulation, including how spatial data are acquired, represented digitally, and spatially aggregated. Also features the nature of space and how it is measured. Descriptive, explanatory, and inferential analyses are covered for point, line, and area data. It captures the latest developments in spatiotemporal simulation with cellular automata and agent-based modelling, and through practical examples discusses how spatial analysis and modelling can be implemented in different computing platforms. A much-needed textbook for a course at upper undergraduate and postgraduate levels.
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Deep Learning for Data Analytics
Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.
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Operations Strategy
This text is designed to be appropriate for Operations Strategy modules at both undergraduate and postgraduate level. It combines knowledge management, relationship management and advances in technology to inform the development of strategic advantage. The text adopts a value chain approach and contains a number of features to aid the learning process - chapter introductions, chapter summaries, further reading and boxed features.
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Advanced Data Analytics in Health
This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are explored throughout the book, while the benefits are also emphasized through the discoveries made possible by linking large datasets. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning. The diversity of datasets is illustrated through chapters on networked data, image processing, and text, in addition to typical structured numerical datasets. While the methods, types of data, and scale have been individually covered elsewhere, by bringing them all together under one “umbrella” the book highlights synergies, while also helping scholars fluidly switch between tools as needed. New challenges and emerging frontiers are also discussed, helping scholars grasp how methods will need to change in response to the latest challenges in health.
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Data-Driven Analytics for the Geological Storage of CO2
Data-driven analytics is enjoying unprecedented popularity among oil and gas professionals. Many reservoir engineering problems associated with geological storage of CO2 require the development of numerical reservoir simulation models. This book is the first to examine the contribution of artificial intelligence and machine learning in data-driven analytics of fluid flow in porous environments, including saline aquifers and depleted gas and oil reservoirs. Drawing from actual case studies, this book demonstrates how smart proxy models can be developed for complex numerical reservoir simulation models. Smart proxy incorporates pattern recognition capabilities of artificial intelligence and machine learning to build smart models that learn the intricacies of physical, mechanical and chemical interactions using precise numerical simulations. This ground breaking technology makes it possible and practical to use high fidelity, complex numerical reservoir simulation models in the design, analysis and optimization of carbon storage in geological formations projects.
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Big Data Analytics for Entrepreneurial Success
In a resolutely practical and data-driven project universe, the digital age changed the way data is collected, stored, analyzed, visualized and protected, transforming business opportunities and strategies. It is important for today's organizations and entrepreneurs to implement a robust data strategy and industrialize a set of ""data-driven"" solutions to utilize big data analytics to its fullest potential.Big Data Analytics for Entrepreneurial Success provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques within business applications. Featuring coverage on a broad range of topics such as algorithms, data collection, and machine learning, this publication provides concrete examples and case studies of successful uses of data-driven projects as well as the challenges and opportunities of generating value from data using analytics. It is ideally designed for entrepreneurs, researchers, business owners, managers, graduate students, academicians, software developers, and IT professionals seeking current research on the essential tools and technologies for organizing, analyzing, and benefiting from big data.
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