Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information retrieval design and implementation questions.

This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms.

The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrieving documents in multiple languages, and using parallel or distributed processing to expedite the search are likewise described.

This edition is a major expansion of the one published in 1998. Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.

This volume presents papers from the 10th Working Conference of the IFIP WG 8.6 on the adoption and diffusion of information systems and technologies. This book explores the dynamics of how some technological innovation efforts succeed while others fail. The book looks to expand the research agenda, paying special attention to the areas of theoretical perspectives, methodologies, and organizational sectors.

The key competing texts are practitioner-focused 'how to' guides, whilst our book combines rigorous theory with practical insight and examples, with authors from both the academic and business world, making it more adoptable as a student text; Unlike other books on the subject, this has a customer focus and an exploration of how big data can add value to customers as well as organisations; Enables readers to move from "big data" to "big solutions" by demonstrating how to integrate data analytics into specific goals and processes for implementation; Highly successful and well regarded both for students and practitioners

- The book shows you what 'data science' actually is and focuses uniquely on how to minimize the negatives of (bad) data science - It discusses the actual place of data science in a variety of companies, and what that means for the process of data science - It provides 'how to' advice to both individuals and managers - It takes a critical approach to data science and provides widely-relatable examples

Provides overview of security challenges of IoT and mitigation techniques with a focus on authorization and access control mechanisms Discusses behavioural analysis of threats and attacks using UML base modelling Covers use of Oauth2.0 Protocol and UMA for connecting web applications Includes Role Based Access Control (RBAC), Discretionary Access Control (DAC), Mandatory Access Control (MAC), and Permission Based Access Control (PBAC) Explores how to provide access to third party web applications through resource server by use of secured and reliable Oauth2.0 framework

This book aims to explore the diverse landscape of journalism in the third decade of the twenty-first century, constantly changing and still dealing with the consequences of a global pandemic. 'Total journalism' is the concept that refers to the renewed and current journalism that employs all available techniques, technologies, and platforms. Authors discuss the innovative nature of journalism, the influence of big data and information disorders, models, professionals and audiences, as well as the challenges of artificial intelligence. The book gives an up-to-date overview of these perspectives on journalistic production and distribution. The effects of misinformation and the challenge of artificial intelligence are of specific relevance in this book. Readers can enjoy with contributions from prestigious experts and researchers who make this book an interesting resource for media professionals and researchers in media and communication studies.

This book gathers contributions from a broad range of jurisdictions, written by practitioners and academics alike, and offers an unparalleled comparative view of key issues in competition law, intellectual property and unfair competition law, with a specific focus on the use of personal data. The first part focuses on the role of competition law in shaping the digital economy. It discusses the use of personal data, the market power of platforms, the assessment of free services, and more broadly the responsibility of dominant companies in the smooth functioning of the digital economy. In turn, the second part sheds light on how the conduct of influencers, native advertising and the use of AI for marketing purposes can be controlled by the law, focusing on the use of personal data and the impact of behavioral advertising on consumers. In this regard, the book brings together the current legal responses across a number of European and other countries, all summarized and elaborated on in the form of two international reports. The LIDC is a long-standing international association that focuses on the interface between competition law and intellectual property law, including unfair competition issues.

Dictation systems, read-aloud software for the blind, speech control of machinery, geographical information systems with speech input and output, and educational software with talking head' artificial tutorial agents are already on the market. The field is expanding rapidly, and new methods and applications emerge almost daily. But good sources of systematic information have not kept pace with the body of information needed for development and evaluation of these systems. Much of this information is widely scattered through speech and acoustic engineering, linguistics, phonetics, and experimental psychology.The Handbook of Multimodal and Spoken Dialogue Systems presents current and developing best practice in resource creation for speech input/output software and hardware. This volume brings experts in these fields together to give detailed how to' information and recommendations on planning spoken dialogue systems, designing and evaluating audiovisual and multimodal systems, and evaluating consumer off-the-shelf products.In addition to standard terminology in the field, the following topics are covered in depth: How to collect high quality data for designing, training, and evaluating multimodal and speech dialogue systems; How to evaluate real-life computer systems with speech input and output; How to describe and model human-computer dialogue precisely and in depth.Also included: A fully searchable CD-ROM containing a hypertext version of the book in HTML format for fast look-up of specific points, convenient desktop use, and lightweight mobile reference; and The first systematic medium-scale compendium of terminology with definitions.This handbook has been especially designed for theneeds of development engineers, decision-makers, researchers, and advanced level students in the fields of speech technology, multimodal interfaces, multimedia, computational linguistics, and phonetics.

This book aims through 11 chapters discussing the problems and challenges and some future research points from the recent technologies point of view such as artificial intelligence and the Internet of things (IoT) that can help the environment and healthcare sectors reducing COVID-19.

This edited book is a collection of selected research papers presented at the 2021 2nd International Conference on Artificial Intelligence in Education Technology (AIET 2021), held in Wuhan, China on July 2-4, 2021. AIET establishes a platform for AI in education researchers to present research, exchange innovative ideas, propose new models, as well as demonstrate advanced methodologies and novel systems. Rapid developments in artificial intelligence (AI) and the disruptive potential of AI in educational use has drawn significant attention from the education community in recent years. For educators entering this uncharted territory, many theoretical and practical questions concerning AI in education are raised, and issues on AI's technical, pedagogical, administrative and socio-cultural implications are being debated. The book provides a comprehensive picture of the current status, emerging trends, innovations, theory, applications, challenges and opportunities of current AI in education research. This timely publication is well-aligned with UNESCO's Beijing Consensus on Artificial Intelligence (AI) and Education. It is committed to exploring how best to prepare our students and harness emerging technologies for achieving the Education 2030 Agenda as we move towards an era in which AI is transforming many aspects of our lives. Providing a broad coverage of recent technology-driven advances and addressing a number of learning-centric themes, the book is an informative and useful resource for researchers, practitioners, education leaders and policy-makers who are involved or interested in AI and education.

This book reviews the state of the art of big data analysis and smart city. It includes issues which pertain to signal processing, probability models, machine learning, data mining, database, data engineering, pattern recognition, visualisation, predictive analytics, data warehousing, data compression, computer programming, smart city, etc. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Data science inspires novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and social science. Papers in this book were the outcome of research conducted in this field of study. The latter makes use of applications and techniques related to data analysis in general and big data and smart city in particular. The book appeals to advanced undergraduate and graduate students, postdoctoral researchers, lecturers and industrial researchers, as well as anyone interested in big data analysis and smart city.

Tremendous growth in healthcare treatment techniques and methods has led to the emergence of numerous storage and communication problems and need for security among vendors and patients. This book brings together latest applications and state-of-the-art developments in healthcare sector using Blockchain technology. It explains how blockchain can enhance security, privacy, interoperability, and data accessibility including AI with blockchains, blockchains for medical imaging to supply chain management, and centralized management/clearing houses alongside DLT. Features: Includes theoretical concepts, empirical studies and detailed overview of various aspects related to development of healthcare applications from a reliable, trusted, and secure data transmission perspective. Provide insights on business applications of Blockchain, particularly in the healthcare sector. Explores how Blockchain can solve the transparency issues in the clinical research. Discusses AI with Blockchains, ranging from medical imaging to supply chain management. Reviews benchmark testing of AI with Blockchains and its impacts upon medical uses. This book aims at researchers and graduate students in healthcare information systems, computer and electrical engineering.

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.

Language ability is a unique human trait, and it is indispensable throughout the human life cycle. Blockchain on the other hand, is an innovation that will transform production relationships, change collaboration models and distribution of benefits between people. Language and blockchain seem to have no intersection, yet they are bewilderingly similar in certain ways.When Language Meets Blockchain leads us into an exploratory journey to discover the possibilities of integrating blockchain technology with the language services industry. The author discusses how blockchain technology enables translators to realise their full potential and describes how the role of language can be elevated from a general tool to a driving force through a new concept called Cross-Linguistic Capability. This is a concept that will have very intriguing and beneficial implications for global economic activities.It is demonstrated that language is more than just a tool, it is also a resource and a form of capability. This presents opportunities for cross-linguistic and cross-cultural communications in the era of blockchain, to enable the convergence of linguistic capability with blockchain technology and artificial intelligence. The book's perspective on how the language services industry could adapt to times to embrace blockchain technology for industrial transformation, is both forwarding-looking and value enhancing.

This book brings together the contributions of the 6th International Conference on Smart Learning Ecosystems and Regional Development, which aims at promoting reflection and discussion concerning R&D work, policies, case studies, entrepreneur experiences with a special focus on understanding the relevance of smart learning ecosystems (e.g., schools, campus, working places, informal learning contexts, etc.) for regional development and social innovation and how the effectiveness of the relation of citizens and smart ecosystems can be boosted. This forum has a special interest in understanding how technology-mediated instruments can foster the citizen's engagement with learning ecosystems and territories, namely by understanding innovative human-centric design and development models/techniques, education/training practices, informal social learning, innovative citizen-driven policies, technology-mediated experiences and their impact. This set of concerns will contribute to foster the social innovation sectors and ICT and economic development and deployment strategies alongside new policies for smarter proactive citizens. Chapter "Robots as My Future Colleagues: Changing Attitudes Toward Collaborative Robots by Means of Experience-Based Workshops" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This volume constitutes the refereed and revised post-conference proceedings of the 4th IFIP TC 5 DCITDRR International Conference on Information Technology in Disaster Risk Reduction, ITDRR 2019, in Kyiv, Ukraine, in October 2019. The 17 full papers and 2 short papers presented were carefully reviewed and selected from 53 submissions. The papers focus on various aspects and challenges of coping with disaster risk reduction. The main topics include areas such as natural disasters, big data, cloud computing, Internet of Things, mobile computing, emergency management, disaster information processing, and disaster risk assessment and management.

Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining."

Data processing has become essential to modern civilization. The original data for this processing comes from measurements or from experts, and both sources are subject to uncertainty. Traditionally, probabilistic methods have been used to process uncertainty. However, in many practical situations, we do not know the corresponding probabilities: in measurements, we often only know the upper bound on the measurement errors; this is known as interval uncertainty. In turn, expert estimates often include imprecise (fuzzy) words from natural language such as "small"; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination describe state-of-the-art developments in their research areas. Accordingly, the book offers a valuable guide for researchers and practitioners interested in data processing under uncertainty, and an introduction to the latest trends and techniques in this area, suitable for graduate students.

The quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of 'big data', 'Data Science', and 'smart cities' changing the world, with the Harvard Business Review describing Data Science as the "sexiest job of the 21st century". Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues. "Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds". - Tom van Vuren, Divisional Director, Mott MacDonald "WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you too can now study via this book." - Leighton Cardwell, Technical Director, WSP. "From processing and analysing large datasets, to automation of modelling tasks sometimes requiring different software packages to "talk" to each other, to data visualization, SYSTRA employs a range of techniques and tools to provide our clients with deeper insights and effective solutions. This book does an excellent job in giving you the skills to manage, interrogate and analyse databases, and develop powerful presentations. Another important publication from ITS Leeds." - Fitsum Teklu, Associate Director (Modelling & Appraisal) SYSTRA Ltd "Urban planning has relied for decades on statistical and computational practices that have little to do with mainstream data science. Information is still often used as evidence on the impact of new infrastructure even when it hardly contains any valid evidence. This book is an extremely welcome effort to provide young professionals with the skills needed to analyse how cities and transport networks actually work. The book is also highly relevant to anyone who will later want to build digital solutions to optimise urban travel based on emerging data sources". - Yaron Hollander, author of "Transport Modelling for a Complete Beginner"

Data science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems. This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era. Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0 is useful for the research community, start-up entrepreneurs, academicians, data-centered industries, and professeurs who are interested in exploring innovations in varied applications and the areas of data science.

A database management system (DBMS) is a collection of programs that enable users to create and maintain a database; it also consists of a collection of interrelated data and a set of programs to access that data. Hence, a DBMS is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications. The primary goal of a DBMS is to provide an environment that is both convenient and efficient to use in retrieving and storing database information. It is an interface between the user of application programs, on the one hand, and the database, on the other. The objective of Database Management System: An Evolutionary Approach, is to enable the learner to grasp a basic understanding of a DBMS, its need, and its terminologies discern the difference between the traditional file-based systems and a DBMS code while learning to grasp theory in a practical way study provided examples and case studies for better comprehension This book is intended to give under- and postgraduate students a fundamental background in DBMSs. The book follows an evolutionary learning approach that emphasizes the basic concepts and builds a strong foundation to learn more advanced topics including normalizations, normal forms, PL/SQL, transactions, concurrency control, etc. This book also gives detailed knowledge with a focus on entity-relationship (ER) diagrams and their reductions into tables, with sufficient SQL codes for a more practical understanding.

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 book examines recent methods for data-driven fault diagnosis of multimode continuous processes. It formalizes, generalizes, and systematically presents the main concepts, and approaches required to design fault diagnosis methods for multimode continuous processes. The book provides both theoretical and practical tools to help readers address the fault diagnosis problem by drawing data-driven methods from at least three different areas: statistics, unsupervised, and supervised learning.

This book introduces computational advertising, and Internet monetization. It provides a macroscopic understanding of how consumer products in the Internet era push user experience and monetization to the limit. Part One of the book focuses on the basic problems and background knowledge of online advertising. Part Two targets the product, operations, and sales staff, as well as high-level decision makers of the Internet products. It explains the market structure, trading models, and the main products in computational advertising. Part Three targets systems, algorithms, and architects, and focuses on the key technical challenges of different advertising products. Features * Introduces computational advertising and Internet monetization * Covers data processing, utilization, and trading * Uses business logic as the driving force to explain online advertising products and technology advancement * Explores the products and the technologies of computational advertising, to provide insights on the realization of personalization systems, constrained optimization, data monetization and trading, and other practical industry problems * Includes case studies and code snippets

"In our increasingly digitally enabled education world, analytics used ethically, strategically, and with care holds the potential to help more and more diverse students be more successful on higher education journeys than ever before. Jay Liebowitz and a cadre of the fields best 'good trouble' makers in this space help shine a light on the possibilities, potential challenges, and the power of learning together in this work." -Mark David Milliron, Ph.D., Senior Vice President and Executive Dean of the Teachers College, Western Governors University Due to the COVID-19 pandemic and its aftereffects, we have begun to enter the "new normal" of education. Instead of online learning being an "added feature" of K-12 schools and universities worldwide, it will be incorporated as an essential feature in education. There are many questions and concerns from parents, students, teachers, professors, administrators, staff, accrediting bodies, and others regarding the quality of virtual learning and its impact on student learning outcomes. Online Learning Analytics is conceived on trying to answer the questions of those who may be skeptical about online learning. Through better understanding and applying learning analytics, we can assess how successful learning and student/faculty engagement, as examples, can contribute towards producing the educational outcomes needed to advance student learning for future generations. Learning analytics has proven to be successful in many areas, such as the impact of using learning analytics in asynchronous online discussions in higher education. To prepare for a future where online learning plays a major role, this book examines: Data insights for improving curriculum design, teaching practice, and learning Scaling up learning analytics in an evidence-informed way The role of trust in online learning. Online learning faces very real philosophical and operational challenges. This book addresses areas of concern about the future of education and learning. It also energizes the field of learning analytics by presenting research on a range of topics that is broad and recognizes the humanness and depth of educating and learning.