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

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.

Significant progression and usage of Internet innovations has caused a need for streamlining past, present, and future database technologies. Principle Advancements in Database Management Technologies: New Applications and Frameworks presents exemplary research in a variety of areas related to database development, technology, and use. This authoritative reference source presents innovative approaches by leading international experts to serve as the primary database management source for researchers, practitioners, and academicians.

Machine Learning, Cyber Security, and Blockchain in Smart Environment: Application and Challenges provides far-reaching insights into the recent techniques forming the backbone of smart environments, and addresses the vulnerabilities that give rise to the challenges in real-word implementation. The book focuses on the benefits related to the emerging applications such as machine learning, blockchain and cyber security. Key Features: Introduces the latest trends in the fields of machine learning, blockchain and cyber security Discusses the fundamentals, challenges and architectural overviews with concepts Explores recent advancements in machine learning, blockchain, and cyber security Examines recent trends in emerging technologies This book is primarily aimed at graduates, researchers, and professionals working in the areas of machine learning, blockchain, and cyber security.

Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

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.

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.

This book covers the basics of semantic web technologies and indexing languages, and describes their contribution to improve methods of formal knowledge representation and reasoning. The methodologies included combine the specifics of indexing languages, Web representation languages and intersystem relations, and explain their contribution to search functionalities in information retrieval scenarios. An example oriented discussion, considering aspects of conceptual and semantic interoperability in processes of subject querying and knowledge exploration is provided. The book is relevant to information scientists, knowledge workers and indexers. It provides a suitable combination of theoretical foundations and practical applications.

Databases have become an integral part of modern life. Today's society is an information-driven society, and database technology has a direct impact on all aspects of daily life. Decisions are routinely made by organizations based on the information collected and stored in databases. Database management systems such as Oracle are crucial to apply data in industrial or commercial systems. Equally crucial is a graphical user interface (GUI) to enable users to access and manipulate data in databases. The Apache NetBeans IDE with Java is an ideal candidate for developing a GUI with programming functionality. Oracle Database Programming with Java: Ideas, Designs, and Implementations is written for college students and software programmers who want to develop practical and commercial database programming with Java and relational databases such as Oracle Database XE 18c. The book details practical considerations and applications of database programming with Java and is filled with authentic examples as well as detailed explanations. Advanced topics in Java Web, like Java Web Applications and Java Web Services, are covered in real project examples to show how to handle the database programming issues in the Apache NetBeans IDE environment. This book features: A real sample database, CSE _ DEPT, which is built with Oracle SQL Developer, provided and used throughout the book Step by step, detailed illustrations and descriptions of how to design and build a practical relational database Fundamental and advanced Java database programming techniques practical to both beginning students and experienced programmers Updated Java desktop and Web database programming techniques, such as Java Enterprise Edition 7, JavaServer Pages, JavaServer Faces, Enterprise Java Beans, Web applications and Web services, including GlassFish and Tomcat Web servers More than 30 real database programming projects with detailed illustrations Actual JDBC APIs and JDBC drivers, along with code explanations Homework and selected solutions for each chapter to strengthen and improve students' learning and understanding of the topics they have studied

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.

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

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.

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.

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.

- Curating Social Data - Summarizing Social Data - Analyzing Social Data - Social Data Analytics Applications: Trust, Recommender Systems, Cognitive Analytics

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.

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.

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 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.

Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia's DSCI100: Introduction to Data Science course.

Maintaining the advanced technical focus found in Developing Essbase Applications, this second volume is another collaborative effort by some of the best and most experienced Essbase practitioners from around the world. Developing Essbase Applications: Hybrid Techniques and Practices reviews technology areas that are much-discussed but still very new, including Exalytics and Hybrid Essbase. Covering recent improvements to the Essbase engine, the book illustrates the impact of new reporting and analysis tools and also introduces advanced Essbase best practices across a variety of features, functions, and theories. Some of this book's chapters are in the same vein as the previous volume: hardware, engines, and languages. Others cover new ground with Oracle Business Intelligence Enterprise Edition, design philosophy, benchmarking concepts, and multiple client tools. As before, these subjects are covered from both the technical and best practice perspectives. This updated volume continues in the tradition of its bestselling predecessor by defining, investigating, and explaining Essbase concepts like no other resource. It also includes use cases that transform abstract theory into practical examples you can easily relate to your own Essbase environment. Illustrating the recent expansion of Essbase functionality, this book provides the up-to-date understanding you need to explore the full depth of the Essbase technology stack. Although the book presents detailed tutorial chapters that can be read on their own, reading the entire book will provide you with a similar understanding as some of the most experienced Essbase practitioners from around the world.

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.