This book provides a concise but comprehensive guide to the disciplines of database design, construction, implementation, and management. Based on the authors' professional experience in the software engineering and IT industries before making a career switch to academia, the text stresses sound database design as a necessary precursor to successful development and administration of database systems. The discipline of database systems design and management is discussed within the context of the bigger picture of software engineering. Students are led to understand from the outset of the text that a database is a critical component of a software infrastructure, and that proper database design and management is integral to the success of a software system. Additionally, students are led to appreciate the huge value of a properly designed database to the success of a business enterprise. The text was written for three target audiences. It is suited for undergraduate students of computer science and related disciplines who are pursuing a course in database systems, graduate students who are pursuing an introductory course to database, and practicing software engineers and information technology (IT) professionals who need a quick reference on database design. Database Systems: A Pragmatic Approach, 3rd Edition discusses concepts, principles, design, implementation, and management issues related to database systems. Each chapter is organized into brief, reader-friendly, conversational sections with itemization of salient points to be remembered. This pragmatic approach includes adequate treatment of database theory and practice based on strategies that have been tested, proven, and refined over several years. Features of the third edition include: Short paragraphs that express the salient aspects of each subject Bullet points itemizing important points for easy memorization Fully revised and updated diagrams and figures to illustrate concepts to enhance the student's understanding Real-world examples Original methodologies applicable to database design Step-by-step, student-friendly guidelines for solving generic database systems problems Opening chapter overviews and concluding chapter summaries Discussion of DBMS alternatives such as the Entity-Attributes-Value model, NoSQL databases, database-supporting frameworks, and other burgeoning database technologies A chapter with sample assignment questions and case studies This textbook may be used as a one-semester or two-semester course in database systems, augmented by a DBMS (preferably Oracle). After its usage, students will come away with a firm grasp of the design, development, implementation, and management of a database system.

This book gathers the proceedings of the Third International Conference on Computational Advancement in Communication Circuits and Systems (ICCACCS 2020), organized virtually by Narula Institute of Technology, Kolkata, India. The book presents peer-reviewed papers that highlight new theoretical and experimental findings in the fields of electronics and communication engineering, including interdisciplinary areas like advanced computing, pattern recognition and analysis, and signal and image processing. The respective papers cover a broad range of principles, techniques, and applications in microwave devices, communication and networking, signal and image processing, computations and mathematics, and control.

This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics. The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book. Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed. The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a "Python corner," which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.

"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).

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.

Security Analytics for the Internet of Everything compiles the latest trends, technologies, and applications in this emerging field. It includes chapters covering emerging security trends, cyber governance, artificial intelligence in cybersecurity, and cyber challenges. Contributions from leading international experts are included. The target audience for the book is graduate students, professionals, and researchers working in the fields of cybersecurity, computer networks, communications, and the Internet of Everything (IoE). The book also includes some chapters written in a tutorial style so that general readers can easily grasp some of the ideas.

* Centers the experiences of designers at every stage of their careers and development. * Defines and explains the idea of professional identity for designers across contexts. * Concludes segments with specific takeaways, reflection activities, and quotes from real-world designers.

Blockchain technology is defined as a decentralized system of distributed registers that are used to record data transactions on multiple computers. The reason this technology has gained popularity is that you can put any digital asset or transaction in the blocking chain, the industry does not matter. Blockchain technology has infiltrated all areas of our lives, from manufacturing to healthcare and beyond. Cybersecurity is an industry that has been significantly affected by this technology and may be more so in the future. Blockchain for Cybersecurity and Privacy: Architectures, Challenges, and Applications is an invaluable resource to discover the blockchain applications for cybersecurity and privacy. The purpose of this book is to improve the awareness of readers about blockchain technology applications for cybersecurity and privacy. This book focuses on the fundamentals, architectures, and challenges of adopting blockchain for cybersecurity. Readers will discover different applications of blockchain for cybersecurity in IoT and healthcare. The book also includes some case studies of the blockchain for e-commerce online payment, retention payment system, and digital forensics. The book offers comprehensive coverage of the most essential topics, including: Blockchain architectures and challenges Blockchain threats and vulnerabilities Blockchain security and potential future use cases Blockchain for securing Internet of Things Blockchain for cybersecurity in healthcare Blockchain in facilitating payment system security and privacy This book comprises a number of state-of-the-art contributions from both scientists and practitioners working in the fields of blockchain technology and cybersecurity. It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on the blockchain for cybersecurity and privacy.

Unique selling point: Focuses solely on entity-relationship model diagramming and design Core audience: Undergraduate CS students and professionals Place in the market: Undergraduate textbook

The two volumes IFIP AICT 551 and 552 constitute the refereed proceedings of the 15th IFIP WG 9.4 International Conference on Social Implications of Computers in Developing Countries, ICT4D 2019, held in Dar es Salaam, Tanzania, in May 2019. The 97 revised full papers and 2 short papers presented were carefully reviewed and selected from 185 submissions. The papers present a wide range of perspectives and disciplines including (but not limited to) public administration, entrepreneurship, business administration, information technology for development, information management systems, organization studies, philosophy, and management. They are organized in the following topical sections: communities, ICT-enabled networks, and development; digital platforms for development; ICT for displaced population and refugees. How it helps? How it hurts?; ICT4D for the indigenous, by the indigenous and of the indigenous; local technical papers; pushing the boundaries - new research methods, theory and philosophy in ICT4D; southern-driven human-computer interaction; sustainable ICT, informatics, education and learning in a turbulent world - "doing the safari way".

Computer and communication networks are among society's most important infrastructures. The internet, in particular, is a giant global network of networks with central control or administration. It is a paradigm of a complex system, where complexity may arise from different sources: topological structure, network evolution, connection and node diversity, and /or dynamical evolution. This is the first book entirely devoted to the new and emerging field of nonlinear dynamics of TCP/IP networks. It addresses both scientists and engineers working in the general field of communication networks.

This book focusses on the Internet of Things (IoT) and Data Mining for Modern Engineering and Healthcare Applications and the recent technological advancements in Microwave Engineering, Communication and applicability of newly developed Solid State Technologies in Bio-medical Engineering and Health-Care. The Reader will be able to know the recent advancements in Microwave Engineering including novel techniques in Microwave Antenna Design and various aspects of Microwave Propagation. This book aims to showcase, the various aspects of Communication, Networking, Data Mining, Computational Biology, Bioinformatics, Bio-Statistics and Machine Learning. In this book, recent trends in Solid State Technologies, VLSI and applicability of modern Electronic Devices in Bio-informatics and Health-Care is focused. Furthermore, this book showcases the modern optimization techniques in Power System Engineering, Machine Design and Power Systems. This Book highlights the Internet of Things (IoT) and Data Mining for Modern Engineering and Healthcare Applications and the recent technological advancements in Microwave Engineering, Communication and applicability of newly developed Solid State Technologies in Bio-medical Engineering and Health-Care for day-to-day applications. Societal benefits of Microwave Technologies for smooth and hustle-free life are also areas of major focus. Microwave Engineering includes recent advancements and novel techniques in Microwave Antenna Design and various aspects of Microwave Propagation. Day-to-Day applicability of modern communication and networking technologies are a matter of prime concern. This book aims to showcase, the various aspects of Communication, Networking, Data Mining, Computational Biology, Bioinformatics, Bio-Statistics and Machine Learning. Role of Solid Sate Engineering in development of modern electronic gadgets are discussed. In this book, recent trends in Solid State Technologies, VLSI and applicability of modern Electronic Devices in Bio-informatics and Biosensing Devices for Smart Health care are also discussed. Features: This book features Internet of Things (IoT) and Data Mining for Modern Engineering and Healthcare Applications and the recent technological advancements in Microwave Engineering, Communication and applicability of newly developed Solid State Technologies in Bio-medical Engineering and Smart Health-Care Technologies Showcases the novel techniques in Internet of Things (IoT) integrated Microwave Antenna Design and various aspects of Microwave Communication Highlights the role of Internet of Things (IoT) various aspects of Communication, Networking, Data Mining, Computational Biology, Bioinformatics, Bio-Statistics and Machine Learning Reviews the role of Internet of Things (IoT) in Solid State Technologies, VLSI and applicability of modern Electronic Devices in Bio-informatics and Health-Care In this book, role of Internet of Things (IoT) in Power System Engineering, Optics, RF and Microwave Energy Harvesting and Smart Biosensing Technologies are also highlighted

This book is the first publication to give a comprehensive, structured treatment to the important topic of situational awareness in cyber defense. It presents the subject in a logical, consistent, continuous discourse, covering key topics such as formation of cyber situational awareness, visualization and human factors, automated learning and inference, use of ontologies and metrics, predicting and assessing impact of cyber attacks, and achieving resilience of cyber and physical mission. Chapters include case studies, recent research results and practical insights described specifically for this book. Situational awareness is exceptionally prominent in the field of cyber defense. It involves science, technology and practice of perception, comprehension and projection of events and entities in cyber space. Chapters discuss the difficulties of achieving cyber situational awareness - along with approaches to overcoming the difficulties - in the relatively young field of cyber defense where key phenomena are so unlike the more conventional physical world. Cyber Defense and Situational Awareness is designed as a reference for practitioners of cyber security and developers of technology solutions for cyber defenders. Advanced-level students and researchers focused on security of computer networks will also find this book a valuable resource.

Business Process Management (BPM) has become one of the most widely used approaches for the design of modern organizational and information systems. The conscious treatment of business processes as significant corporate assets has facilitated substantial improvements in organizational performance but is also used to ensure the conformance of corporate activities. This Handbook presents in two volumes the contemporary body of knowledge as articulated by the world's leading BPM thought leaders. This second volume focuses on the managerial and organizational challenges ofBPM such as strategic and cultural alignment, governance and the education of BPM stakeholders. As such, this book provides concepts and methodologies for the integration of BPM. Each chapter has been contributed by leading international experts. Selected case studies complement their views and lead to a summary of BPM expertise that is unique in its coverage of the most critical success factors of BPM.

The second edition of this handbook has been significantly revised and extended. Each chapter has been updated to reflect the most current developments. This includes in particular new technologies such as in-memory data and process management, social media and networks. A further focus of this revised and extended edition is on the actual deployment of the proposed theoretical concepts. This volume includes a number of entire new chapters from some of the world's leading experts in the domain of BPM.

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In this era of heterogeneous and distributed data sources, ranging from semistructured documents to knowledge about coordination processes or workflows, logic provides a rich set of tools and techniques with which to address the questions of how to represent, query and reason about complex data. This book provides a state-of-the-art overview of research on the application of logic-based methods to information systems, covering highly topical and emerging fields: XML programming and querying, intelligent agents, workflow modeling and verification, data integration, temporal and dynamic information, data mining, authorization, and security. It provides both scientists and graduate students with a wealth of material and references for their own research and education.

The volume includes a collection of peer-reviewed contributions from among those presented at the main conference organized yearly by the Mexican Statistical Association (AME) and every two years by a Latin-American Confederation of Statistical Societies. For the 2018 edition, particular attention was placed on the analysis of highly complex or large data sets, which have come to be known as "big data". Statistical research in Latin America is prolific and research networks span within and outside the region. The goal of this volume is to provide access to selected works from Latin-American collaborators and their research networks to a wider audience. New methodological advances, motivated in part by the challenges of a data-driven world and the Latin American context, will be of interest to academics and practitioners around the world.

The Ethics of Artificial Intelligence in Education identifies and confronts key ethical issues generated over years of AI research, development, and deployment in learning contexts. Adaptive, automated, and data-driven education systems are increasingly being implemented in universities, schools, and corporate training worldwide, but the ethical consequences of engaging with these technologies remain unexplored. Featuring expert perspectives from inside and outside the AIED scholarly community, this book provides AI researchers, learning scientists, educational technologists, and others with questions, frameworks, guidelines, policies, and regulations to ensure the positive impact of artificial intelligence in learning.

This work provides an assessment of the current state of near field communication (NFC) security, it reports on new attack scenarios, and offers concepts and solutions to overcome any unresolved issues. The work describes application-specific security aspects of NFC based on exemplary use-case scenarios and uses these to focus on the interaction with NFC tags and on card emulation. The current security architectures of NFC-enabled cellular phones are evaluated with regard to the identified security aspects.

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 consists of 35 chapters presenting different theoretical and practical aspects of Intelligent Information and Database Systems. Nowadays both Intelligent and Database Systems are applied in most of the areas of human activities which necessitates further research in these areas. In this book various interesting issues related to the intelligent information models and methods as well as their advanced applications, database systems applications, data models and their analysis and digital multimedia methods and applications are presented and discussed both from the practical and theoretical points of view. The book is organized in four parts devoted to intelligent systems models and methods, intelligent systems advanced applications, database systems methods and applications and multimedia systems methods and applications. The book will be interesting forpractitioners and researchers, especially graduate and PhD students of information technology and computer science, as well more experienced academics and specialists interested in developing and verification of intelligent information, database and multimedia systems models, methods and applications. The readers of this volume are enabled to find many inspiring ideas and motivating practical examples that will help them in the current and future work."

This book systematically introduces readers to the development of simulation models as well as the implementation and evaluation of simulation experiments with Tecnomatix Plant Simulation. Intended for all Plant Simulation users whose work involves complex tasks, it also offers an easy start for newcomers. Particular attention has been paid to introducing the simulation flow language SimTalk and its use in various aspects of simulation. In over 200 examples, the author demonstrates how to combine the blocks for simulation models and how to employ SimTalk in complex control and analysis tasks. The content ranges from a description of the basic functions of the material flow blocks to more advanced topics such as the implementation of database-supported warehouse control by using the SQLite interface, and the exchange of data using XML, ActiveX, COM or DDE.

Getting Started with Natural Language Processing is a hands-on guide filled with everything you need to get started with NLP in a friendly, understandable tutorial. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. By following the numerous Python-based examples and real-world case studies, you'll apply NLP to search applications, extracting meaning from text, sentiment analysis, user profiling, and more. When you're done, you'll have a solid grounding in NLP that will serve as a foundation for further learning. Key Features * Extracting information from raw text * Named entity recognition * Automating summarization of key facts * Topic labeling For beginners to NLP with basic Python skills. About the technology Natural Language Processing is a set of data science techniques that enable machines to make sense of human text and speech. Advances in machine learning and deep learning have made NLP more efficient and reliable than ever, leading to a huge number of new tools and resources. From improving search applications to sentiment analysis, the possible applications of NLP are vast and growing. Ekaterina Kochmar is an Affiliated Lecturer and a Senior Research Associate at the Natural Language and Information Processing group of the Department of Computer Science and Technology, University of Cambridge. She holds an MA degree in Computational Linguistics, an MPhil in Advanced Computer Science, and a PhD in Natural Language Processing.

Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases. The contributing authors consist of some of the leading scholars in the fields of rough sets, data mining, machine learning and other areas of artificial intelligence. Among the list of contributors are Z. Pawlak, J Grzymala-Busse, K. Slowinski, and others. Rough Sets and Data Mining: Analysis of Imprecise Data will be a useful reference work for rough set researchers, data base designers and developers, and for researchers new to the areas of data mining and rough sets.

This comprehensive overview of IoT systems architecture includes in-depth treatment of all key components: edge, communications, cloud, data processing, security, management, and uses. Internet of Things: Concepts and System Design provides a reference and foundation for students and practitioners that they can build upon to design IoT systems and to understand how the specific parts they are working on fit into and interact with the rest of the system. This is especially important since IoT is a multidisciplinary area that requires diverse skills and knowledge including: sensors, embedded systems, real-time systems, control systems, communications, protocols, Internet, cloud computing, large-scale distributed processing and storage systems, AI and ML, (preferably) coupled with domain experience in the area where it is to be applied, such as building or manufacturing automation. Written in a reader-minded approach that starts by describing the problem (why should I care?), placing it in context (what does this do and where/how does it fit in the great scheme of things?) and then describing salient features of solutions (how does it work?), this book covers the existing body of knowledge and design practices, but also offers the author's insights and articulation of common attributes and salient features of solutions such as IoT information modeling and platform characteristics.

Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.