This textbook helps future data analysts comprehend aggregation function theory and methods in an accessible way, focusing on a fundamental understanding of the data and summarization tools. Offering a broad overview of recent trends in aggregation research, it complements any study in statistical or machine learning techniques. Readers will learn how to program key functions in R without obtaining an extensive programming background. Sections of the textbook cover background information and context, aggregating data with averaging functions, power means, and weighted averages including the Borda count. It explains how to transform data using normalization or scaling and standardization, as well as log, polynomial, and rank transforms. The section on averaging with interaction introduces OWS functions and the Choquet integral, simple functions that allow the handling of non-independent inputs. The final chapters examine software analysis with an emphasis on parameter identification rather than technical aspects. This textbook is designed for students studying computer science or business who are interested in tools for summarizing and interpreting data, without requiring a strong mathematical background. It is also suitable for those working on sophisticated data science techniques who seek a better conception of fundamental data aggregation. Solutions to the practice questions are included in the textbook.

Focused on the mechanics of managing environmental data, this book provides guidelines on how to evaluate data requirements, assess tools and techniques, and implement an effective system. Moving beyond the hypothetical, Gerald Burnette illustrates the decision-making processes and the compromises required when applying environmental principles and practices to actual data. Managing Environmental Data explains the basic principles of relational databases, discusses database design, explores user interface options, and examines the process of implementation. Best practices are identified during each portion of the process. The discussion is summarized via the development of a hypothetical environmental data management system. Details of the design help establish a common framework that bridges the gap between data managers, users, and software developers. It is an ideal text for environmental professionals and students. The growth in both volume and complexity of environmental data presents challenges to environmental professionals. Developing better data management skills offers an excellent opportunity to meet these challenges. Gaining knowledge of and experience with data management best practices complements students' more traditional science education, providing them with the skills required to address complex data requirements.

Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.

This book is for anyone who wants to gain an understanding of Blockchain technology and its potential. The book is research-oriented and covers different verticals of Blockchain technology. It discusses the characteristics and features of Blockchain, includes techniques, challenges, and future trends, along with case studies for deeper understanding. Blockchain Technology: Exploring Opportunities, Challenges, and Applications covers the core concepts related to Blockchain technology starting from scratch. The algorithms, concepts, and application areas are discussed according to current market trends and industry needs. It presents different application areas of industry and academia and discusses the characteristics and features of this technology. It also explores the challenges and future trends and provides an understanding of new opportunities. This book is for anyone at the beginner to intermediate level that wants to learn about the core concepts related to Blockchain technology.

The internet has launched the world into an era into which enormous amounts of data are generated every day through technologies with both positive and negative consequences. This often refers to big data . This book explores big data in organisations operating in the criminology and criminal justice fields. Big data entails a major disruption in the ways we think about and do things, which certainly applies to most organisations including those operating in the criminology and criminal justice fields. Big data is currently disrupting processes in most organisations - how different organisations collaborate with one another, how organisations develop products or services, how organisations can identify, recruit, and evaluate talent, how organisations can make better decisions based on empirical evidence rather than intuition, and how organisations can quickly implement any transformation plan, to name a few. All these processes are important to tap into, but two underlying processes are critical to establish a foundation that will permit organisations to flourish and thrive in the era of big data - creating a culture more receptive to big data and implementing a systematic data analytics-driven process within the organisation. Written in a clear and direct style, this book will appeal to students and scholars in criminology, criminal justice, sociology, and cultural studies but also to government agencies, corporate and non-corporate organisations, or virtually any other institution impacted by big data.

This thesis deals with two important and very timely aspects of the future power system operation - assessment of demand flexibility and advanced demand side management (DSM) facilitating flexible and secure operation of the power network. It provides a clear and comprehensive literature review in these two areas and states precisely the original contributions of the research. The book first demonstrates the benefits of data mining for a reliable assessment of demand flexibility and its composition even with very limited observability of the end-users. It then illustrates the importance of accurate load modelling for efficient application of DSM and considers different criteria in designing DSM programme to achieve several objectives of the network performance simultaneously. Finally, it demonstrates the importance of considering realistic assumptions when planning and estimating the success of DSM programs. The findings presented here have both scientific and practical significance; they gained her BSc and MSc degrees in electrical engineering from the University of Belgrade in 2011 and 2012 respectively. She graduated with her PhD from the University of Manchester. She has presented at several conferences, and has won runner-up prizes in poster presentation at three. She has authored or co-authored more than 40 journal, conference and technical papers.provide a basis for further research, and can be used to guide future applications in industry.

The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: * Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. * Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. * Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science.

The use of biometric identification systems is rapidly increasing across the world, owing to their potential to combat terrorism, fraud, corruption and other illegal activities. However, critics of the technology complain that the creation of an extensive central register of personal information controlled by the government will increase opportunities for the state to abuse citizens. There is also concern about the extent to which data about an individual is recorded and kept. This book reviews some of the most current and complex legal and ethical issues relating to the use of biometrics. Beginning with an overview of biometric systems, the book goes on to examine some of the theoretical underpinnings of the surveillance state, questioning whether these conceptual approaches are still relevant, particularly the integration of ubiquitous surveillance systems and devices. The book also analyses the implementation of the world's largest biometric database, Aadhaar, in detail. Additionally, the identification of individuals at border checkpoints in the United States, Australia and the EU is explored, as well as the legal and ethical debates surrounding the use of biometrics regarding: the war on terror and the current refugee crisis; violations of international human rights law principles; and mobility and privacy rights. The book concludes by addressing the collection, use and disclosure of personal information by private-sector entities such as Axciom and Facebook, and government use of these tools to profile individuals. By examining the major legal and ethical issues surrounding the debate on this rapidly emerging technology, this book will appeal to students and scholars of law, criminology and surveillance studies, as well as law enforcement and criminal law practitioners.

This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have become widely used in companies of all sizes. Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks: Community detection (which users on some social media platforms are close friends). Link prediction (who is likely to connect to whom on such platforms). Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests). Influential node detection (which social media users would be the best ambassadors of a specific product). This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path. Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https://www.ryerson.ca/mining-complex-networks/. These not only contain all the experiments presented in the book, but also include additional material. Bogumil Kaminski is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is also an Adjunct Professor at the Data Science Laboratory at Ryerson University. Bogumil is an expert in applications of mathematical modeling to solving complex real-life problems. He is also a substantial open-source contributor to the development of the Julia language and its package ecosystem. Pawel Pralat is a Professor of Mathematics in Ryerson University, whose main research interests are in random graph theory, especially in modeling and mining complex networks. He is the Director of Fields-CQAM Lab on Computational Methods in Industrial Mathematics in The Fields Institute for Research in Mathematical Sciences and has pursued collaborations with various industry partners as well as the Government of Canada. He has written over 170 papers and three books with 130 plus collaborators. Francois Theberge holds a B.Sc. degree in applied mathematics from the University of Ottawa, a M.Sc. in telecommunications from INRS and a PhD in electrical engineering from McGill University. He has been employed by the Government of Canada since 1996 where he was involved in the creation of the data science team as well as the research group now known as the Tutte Institute for Mathematics and Computing. He also holds an adjunct professorial position in the Department of Mathematics and Statistics at the University of Ottawa. His current interests include relational-data mining and deep learning.

"In what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata matters-he vividly illustrates why it matters." -Patricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San Jose State University, USA If data is the language upon which our modern society will be built, then metadata will be its grammar, the construction of its meaning, the building for its content, and the ability to understand what data can be for us all. We are just starting to bring change into the management of the data that connects our experiences. Metadata Matters explains how metadata is the foundation of digital strategy. If digital assets are to be discovered, they want to be found. The path to good metadata design begins with the realization that digital assets need to be identified, organized, and made available for discovery. This book explains how metadata will help ensure that an organization is building the right system for the right users at the right time. Metadata matters and is the best chance for a return on investment on digital assets and is also a line of defense against lost opportunities. It matters to the digital experience of users. It helps organizations ensure that users can identify, discover, and experience their brands in the ways organizations intend. It is a necessary defense, which this book shows how to build.

In this book, Edosa explores common challenges which limit the value that organisations can get from data. What makes his book unique is that he also tackles one of the unspoken barriers to data adoption-fear. Fear of the unknown, fear of the intangible, fear of the investment needed and, yes, fear of losing your job to a machine. With his talent for distilling clarity from complexity, Edosa tackles this and many other challenges. -Tim Carmichael, Chief Data Officer, Chalhoub Group This book offers fresh insight about how to solve the interactional frictions that hamper the flow of data, information and knowledge across organisations. Yet, rather than being stuck with endless polarising debates such as breaking down silos, it shifts focus back towards the ultimate "to what end." -Jacky Wright, Chief Digital Officer (CDO), Microsoft US If you care about AI transformation, empowering people or advancing organisational success in an increasingly digital world, then you should read this book. -Yomi Ibosiola, Chief Data and Analytics Officer, Union Bank A retail giant already struggling due to the Covid-19 pandemic was faced with a disastrous situation when-at the end of a critical investment in an artificial intelligence project that had been meant to save money-it suddenly discovered that its implementation was likely to leave it worse off. An entire critical service stream within an insurer's production system crashed. This critical failure resulted in the detentions of fully insured motorists for allegedly not carrying required insurance. Making Data Work details these two scenarios as well as others illustrating the consequences that arise when organizations do not know how to make data work properly. It is a journey to determine what to do to "make data work" for ourselves and for our organisations. It is a journey to discover how to bring it all together so organisations can enable digital transformation, empower people, and advance organisational success. It is the journey to a world where data and technology finally live up to the hype and deliver better human outcomes, where artificial intelligence can move us from reacting to situations to predicting future occurrences and enabling desirable possibilities.

This new volume illustrates the diverse applications of IoT. The volume addresses the crucial issue of data safekeeping along with the development of a new cryptographic and security technology as well as a range of other advances in IoT. The volume looks at the application of IoT in medical technology and healthcare, including the design of IoT-based mobile healthcare units and a blockchain technique based smart health record system. Other topics include a blended IoT-enabled learning approach through a study employing clustering techniques, an IoT-enabled garbage disposal system with an advanced message notification system through an android application, IoT-based self-healing concrete that uses bacteria and environmental waste, an IoT-enabled trash-the-ash application that regulates flow, and more. The fresh and innovative advances that demonstrate computational intelligence and IoT in practice that are discussed in this volume will be informative for academicians, scholars, scientists, industry professionals, policymakers, government and non-government organizations, and others.

CMDB Systems: Making Change Work in the Age of Cloud and Agile shows you how an integrated database across all areas of an organization's information system can help make organizations more efficient reduce challenges during change management and reduce total cost of ownership (TCO). In addition, this valuable reference provides guidelines that will enable you to avoid the pitfalls that cause CMDB projects to fail and actually shorten the time required to achieve an implementation of a CMDB. Drawing upon extensive experience and using illustrative real world examples, Rick Sturm, Dennis Drogseth and Dan Twing discuss: Unique insights from extensive industry exposure, research and consulting on the evolution of CMDB/CMS technology and ongoing dialog with the vendor community in terms of current and future CMDB/CMS design and plans Proven and structured best practices for CMDB deployments Clear and documented insights into the impacts of cloud computing and other advances on CMDB/CMS futures

With the rapidly advancing fields of Data Analytics and Computational Statistics, it's important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.

This book presents a comprehensive overview of the key topics, best practices, future opportunities and challenges in the Digital Marketing discourse. With contributions from world-renowned experts, the book covers: * Big Data, Artificial Intelligence and Analytics in Digital Marketing * Emerging technologies and how they can enhance User Experience * How 'digital' is changing servicescapes * Issues surrounding ethics and privacy * Current and future issues surrounding Social Media * Key considerations for the future of Digital Marketing * Case studies and examples from real-life organisations Unique in its rigorous, research-driven and accessible approach to the subject of Digital Marketing, this text is valuable supplementary reading for advanced undergraduate and postgraduate students studying Digital and Social Media Marketing, Customer Experience Management, Digital Analytics and Digital Transformation.

This book serves the need for developing an insight and understanding of the cutting-edge innovation in Cloud technology. It provides an understanding of cutting-edge innovations, paradigms, and security by using real-life applications, case studies, and examples. This book provides a holistic view of cloud technology theories, practices, and future applications with real-life examples. It comprehensively explains cloud technology, design principles, development trends, maintaining state-of-the-art cloud computing and software services. It describes how cloud technology can transform the operating contexts of business enterprises. It exemplifies the potential of cloud computing for next-generation computational excellence and the role it plays as a key driver for the 4th industrial revolution in Industrial Engineering and a key driver for manufacturing industries. Researchers, academicians, postgraduates, and industry specialists will find this book of interest.

Introduces design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt and adapt. Free from endless derivations equations are presented and explained strategically, explaining why it is imperative to use them and how they will help in your task at hand. Illustrations and simple explanation help readers visualize and absorb easily, difficult to understand concepts.

Following the introduction of Myspace, Linkedin and Facebook, social networks have become part of people's everyday life. New online social networks appear almost daily developed to attract the attention of Internet users. However, design and implementation standards and guidelines are often sacrificed for innovation resulting in unusable social networking platforms destined to be doomed. Within this context, the present book recommends a design and implementation methodology which will allow future social networking platform designers and developers to work in a scientifically systematic and sound manner to reach their goal. The journey begins with the identification of the pursued objective of the social network and the potential participants. The presentation of various methods and techniques to collect the requirements of the intended user group of the social networking platform follows. Best practices, guidelines and standards that will facilitate the conceptual and physical design of the platform are portrayed. An analysis of advantages and limitations for existing implementation platforms and potential implementation techniques is subsequently offered to facilitate the selection of the most appropriate tool for the implementation of the online social networking website. Various representative visualization techniques are examined in order to enhance the visual representation of the social network participants. Various usability evaluation techniques are then presented to aid in the assessment of usability and improvement of the actual user experience. Finally, future trends in the design and use of social networks are presented.

Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain. Most of the data stored worldwide is legacy data-data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it. The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.

This book presents futuristic trends in computational intelligence including algorithms as applicable to different application domains in health informatics covering bio-medical, bioinformatics, and biological sciences. Latest evolutionary approaches to solve optimization problems under biomedical engineering field are discussed. It provides conceptual framework with a focus on application of computational intelligence techniques in the domain of biomedical engineering and health informatics including real-time issues.

This book provides comprehensive discussion on key topics related to the usage and deployment of software defined networks (SDN) in Internet of Everything applications like, healthcare systems, data centers, edge/fog computing, vehicular networks, intelligent transportation systems, smart grids, smart cities and more. The authors provide diverse solutions to overcome challenges of conventional network binding in various Internet of Everything applications where there is need of an adaptive, agile, and flexible network backbone. The book showcases different deployment models, algorithms and implementations related to the usage of SDN in Internet of Everything applications along with the pros and cons of the same. Even more, this book provides deep insights into the architecture of software defined networking specifically about the layered architecture and different network planes, logical interfaces, and programmable operations. The need of network virtualization and the deployment models for network function virtualization is also included with an aim towards the design of interoperable network architectures by researchers in future. Uniquely, the authors find hands on practical implementation, deployment scenarios and use cases for various software defined networking architectures in Internet of Everything applications like healthcare networks, Internet of Things, intelligent transportation systems, smart grid, underwater acoustic networks and many more. In the end, design and research challenges, open issues, and future research directions are provided in this book for a wide range of readers

This edited book investigates the lack of interoperability in the IoT realm, including innovative research as well as technical solutions to interoperability, integration, and interconnection of heterogeneous IoT systems, at any level. It also explores issues caused by lack of interoperability such as impossibility to plug non-interoperable IoT devices into heterogeneous IoT platforms, impossibility to develop IoT applications exploiting multiple platforms in homogeneous and/or cross domains, slowness of IoT technology introduction at large-scale: discouragement in adopting IoT technology, increase of costs; scarce reusability of technical solutions and difficulty in meeting user satisfaction.

Explores the potential for disruptive technologies in different industries Addresses the challenges associated with the practical implementation or adoption of disruptive technologies Discusses topics related to IoT, Machine Learning, Sensors, Artificial Intelligence and Cloud Computing for Industry 4.0 and 5.0 Includes examples of the implementation of Industry 4.0 and 5.0 in various sectors, including engineering Contains many case studies for easy comprehension.

"This book bridges the fields of health care and data to clarify how to use data to manage pandemics. Written while COVID-19 was raging, it identifies both effective practices and misfires, and is grounded in clear, research-based explanations of pandemics and data strategy....The author has written an essential book for students and professionals in both health care and data. While serving the needs of academics and experts, the book is accessible for the general reader." - Eileen Forrester, CEO of Forrester Leadership Group, Author of CMMI for Services, Guidelines for Superior Service "...Rupa Mahanti explores the connections between data and the human response to the spread of disease in her new book,... She recognizes the value of data and the kind of insight it can bring, while at the same time recognizing that using data to solve problems requires not just technology, but also leadership and courage. This is a book for people who want to better understand the role of data and people in solving human problems." -- Laura Sebastian-Coleman, Author of Meeting the Challenges of Data Quality Management In contrast to the 1918 Spanish flu pandemic which occurred in a non-digital age, the timing of the COVID-19 pandemic intersects with the digital age, characterized by the collection of large amounts of data and sophisticated technologies. Data and technology are being used to combat this digital age pandemic in ways that were not possible in the pre-digital age. Given the adverse impacts of pandemics in general and the COVID-19 pandemic in particular, it is imperative that people understand the meaning, origin of pandemics, related terms, trajectory of a new disease, butterfly effect of contagious diseases, factors governing the pandemic potential of a disease, strategies to combat a pandemic, role of data, data sharing, data strategy, data governance, analytics, and data visualization in managing pandemics, pandemic myths, critical success factors in managing pandemics, and lessons learned. How Data Can Manage Global Health Pandemics: Analyzing and Understanding COVID-19 discusses these elements with special reference to COVID-19. Dr. Rupa Mahanti is a business and data consultant and has expertise in different data management disciplines, business process improvement, regulatory reporting, quality management, and more. She is the author of Data Quality (ASQ Quality Press) and the series Data Governance: The Way Forward (Springer).

This book explores a wide range of emerging cultural, heritage, and other tourism issues that will shape the future of hospitality and tourism research and practice in the digital and innovation era. It offers stimulating new perspectives in the fields of tourism, travel, hospitality, culture and heritage, leisure, and sports within the context of a knowledge society and smart economy. A central theme is the need to adopt a more holistic approach to tourism development that is aligned with principles of sustainability; at the same time, the book critically reassesses the common emphasis on innovation as a tool for growth-led and market-oriented development. In turn, fresh approaches to innovation practices underpinned by ethics and sustainability are encouraged, and opportunities for the exploration of new research avenues and projects on innovation in tourism are highlighted. Based on the proceedings of the Sixth International Conference of the International Association of Cultural and Digital Tourism (IACuDiT) and edited in collaboration with IACuDiT, the book will appeal to a broad readership encompassing academia, industry, government, and other organizations.