This book focuses on the basic theory and methods of multisensor data fusion state estimation and its application. It consists of four parts with 12 chapters. In Part I, the basic framework and methods of multisensor optimal estimation and the basic concepts of Kalman filtering are briefly and systematically introduced. In Part II, the data fusion state estimation algorithms under networked environment are introduced. Part III consists of three chapters, in which the fusion estimation algorithms under event-triggered mechanisms are introduced. Part IV consists of two chapters, in which fusion estimation for systems with non-Gaussian but heavy-tailed noises are introduced. The book is primarily intended for researchers and engineers in the field of data fusion and state estimation. It also benefits for both graduate and undergraduate students who are interested in target tracking, navigation, networked control, etc.

This comprehensive book unveils the working relationship of blockchain and the fog/edge computing. The contents of the book have been designed in such a way that the reader will not only understand blockchain and fog/edge computing but will also understand their co-existence and their collaborative power to solve a range of versatile problems. The first part of the book covers fundamental concepts and the applications of blockchain-enabled fog and edge computing. These include: Internet of Things, Tactile Internet, Smart City; and E-challan in the Internet of Vehicles. The second part of the book covers security and privacy related issues of blockchain-enabled fog and edge computing. These include, hardware primitive based Physical Unclonable Functions; Secure Management Systems; security of Edge and Cloud in the presence of blockchain; secure storage in fog using blockchain; and using differential privacy for edge-based Smart Grid over blockchain. This book is written for students, computer scientists, researchers and developers, who wish to work in the domain of blockchain and fog/edge computing. One of the unique features of this book is highlighting the issues, challenges, and future research directions associated with Blockchain-enabled fog and edge computing paradigm. We hope the readers will consider this book a valuable addition in the domain of Blockchain and fog/edge computing.

This book explores the concepts and role of green computing and its recent developments for making the environment sustainable. It focuses on green automation in disciplines such as computers, nanoscience, information technology, and biochemistry. This book is characterized through descriptions of sustainability, green computing, their relevance to the environment, society, and its applications. Presents how to make the environment sustainable through engineering aspects and green computing Explores concepts and the role of green computing with recent developments Processes green automation linked with various disciplines such as nanoscience, information technology, and biochemistry Explains the concepts of green computing linked with sustainable environment through information technology This book will be of interest to researchers, libraries, students, and academicians that are interested in the concepts of green computing linked with green automation through information technology and their impacts on the future.

This book provides a coherent framework for understanding the essence of complex systems and the nature of digital transformations, analyzes challenges of and patterns in innovative development, and shares a wealth of insights and best practices, resulting in the most extensive coverage of the topic available. In particular, the book's cutting-edge contributions, prepared by scientists, engineers, and field experts,focus on the design, implementation, and evaluation of practical interventions that promote the innovative and sustainable development of complex systems. In addition to sharing a rich collection of cases from around the world, they provide a broad interdisciplinary analysis of collaboration mechanisms, theories and approaches to support and accelerate the development of complex systems.

Diabetes Mellitus (DM, commonly referred to as diabetes, is a metabolic disorder in which there are high blood sugar levels over a prolonged period. Lack of sufficient insulin causes presence of excess sugar levels in the blood. As a result the glucose levels in diabetic patients are more than normal ones. It has symptoms like frequent urination, increased hunger, increase thirst and high blood sugar. There are mainly three types of diabetes namely type-1, type-2 and gestational diabetes. Type-1 DM occurs due to immune system mistakenly attacks and destroys the beta-cells and Type-2 DM occurs due to insulin resistance. Gestational DM occurs in women during pregnancy due to insulin blocking by pregnancy harmones. Among these three types of DM, type-2 DM is more prevalence, and impacting so many millions of people across the world. Classification and predictive systems are actually reliable in the health care sector to explore hidden patterns in the patients data. These systems aid, medical professionals to enhance their diagnosis, prognosis along with remedy organizing techniques. The less percentage of improvement in classifier predictive accuracy is very important for medical diagnosis purposes where mistakes can cause a lot of damage to patient's life. Hence, we need a more accurate classification system for prediction of type-2 DM. Although, most of the above classification algorithms are efficient, they failed to provide good accuracy with low computational cost. In this book, we proposed various classification algorithms using soft computing techniques like Neural Networks (NNs), Fuzzy Systems (FS) and Swarm Intelligence (SI). The experimental results demonstrate that these algorithms are able to produce high classification accuracy at less computational cost. The contributions presented in this book shall attempt to address the following objectives using soft computing approaches for identification of diabetes mellitus. Introuducing an optimized RBFN model called Opt-RBFN. Designing a cost effective rule miner called SM-RuleMiner for type-2 diabetes diagnosis. Generating more interpretable fuzzy rules for accurate diagnosis of type2 diabetes using RST-BatMiner. Developing accurate cascade ensemble frameworks called Diabetes-Network for type-2 diabetes diagnosis. Proposing a Multi-level ensemble framework called Dia-Net for improving the classification accuracy of type-2 diabetes diagnosis. Designing an Intelligent Diabetes Risk score Model called Intelli-DRM estimate the severity of Diabetes mellitus. This book serves as a reference book for scientific investigators who need to analyze disease data and/or numerical data, as well as researchers developing methodology in soft computing field. It may also be used as a textbook for a graduate and post graduate level course in machine learning or soft computing.

Structuring, or, as it is referred to in the title of this book, the art of structuring, is one of the core elements in the discipline of Information Systems. While the world is becoming increasingly complex, and a growing number of disciplines are evolving to help make it a better place, structure is what is needed in order to understand and combine the various perspectives and approaches involved. Structure is the essential component that allows us to bridge the gaps between these different worlds, and offers a medium for communication and exchange. The contributions in this book build these bridges, which are vital in order to communicate between different worlds of thought and methodology - be it between Information Systems (IS) research and practice, or between IS research and other research disciplines. They describe how structuring can be and should be done so as to foster communication and collaboration. The topics covered reflect various layers of structure that can serve as bridges: models, processes, data, organizations, and technologies. In turn, these aspects are complemented by visionary outlooks on how structure influences the field.

User Engagement (UE) is a complex concept to investigate. The purpose of this book is not to constrain UE to one perspective, but to offer a well-rounded appreciation for UE across various domains and disciplines. The text begins with two foundational chapters that describe theoretical and methodological approaches to user engagement; the remaining contributions examine UE from different disciplinary perspectives and across a range of computer-mediated environments, including social and communications media, online search, eLearning, games, and eHealth. The book concludes by bringing together the cross-disciplinary perspectives presented in each chapter and proposing an agenda for future research in this area. The book will appeal to established and emerging academic and industry researchers looking to pursue research and its challenges. This includes scholars at all levels with an interest in user engagement with digital media, from students to experienced researchers, and professionals in the fields of computer science, web technology, information science, museum studies, learning and health sciences, human-computer interaction, information architecture and design, and creative arts.

Presents the knowledge and history of Bitcoin Offers recent Blockchain applications Discusses developing working code for real-world Blockchain applications Includes many real-life examples Covers going from the original bitcoin protocol to the second generation Ethereum platform

This book covers challenges and solutions in establishing Industry 4.0 standards for Internet of Things. It proposes a clear view about the role of Internet of Things in establishing standards. The sensor design for industrial problem, challenges faced, and solutions are all addressed. The concept of digital twin and complexity in data analytics for predictive maintenance and fault prediction is also covered. The book is aimed at existing problems faced by the industry at present, with the goal of cost-efficiency and unmanned automation. It also concentrates on predictive maintenance and predictive failures. In addition, it includes design challenges and a survey of literature.

Data Structures and Abstractions with Java is suitable for one- or two-semester courses in data structures (CS-2) in the departments of Computer Science, Computer Engineering, Business, and Management Information Systems. This book is also useful for programmers and software engineers interested in learning more about data structures and abstractions. This is the most student-friendly data structures text available that introduces ADTs in individual, brief chapters - each with pedagogical tools to help students master each concept. Using the latest features of Java, this unique object-oriented presentation makes a clear distinction between specification and implementation to simplify learning, while providing maximum classroom flexibility. Teaching and Learning Experience This book will provide a better teaching and learning experience-for you and your students. It will help: Aid comprehension and facilitate teaching with an approachable format and content organization: Material is organized into small segments that focus a reader's attention and provide greater instructional flexibility. Support learning with student-friendly pedagogy: In-text and online features help students master the material.

Learning analytics is one of the most important research issues in the field of educational technology. By analyzing logs and records in educational databases and systems, it can provide useful information to teachers, learners, and decision makers - information which they can use to improve teaching strategies, learning performances, and educational policies. However, it is a great challenge for most researchers to efficiently analyze educational data in a meaningful way. This book presents various learning analytics approaches and applications, including the process of determining the coding scheme, analyzing the collected data, and interpreting the findings. This book was originally published as a special issue of Interactive Learning Environments.

Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets. Features Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks.

Challenges in Design and Implementation of Middlewares for Real-Time Systems brings together in one place important contributions and up-to-date research results in this fast moving area. Challenges in Design and Implementation of Middlewares for Real-Time Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

In this book, the authors first address the research issues by providing a motivating scenario, followed by the exploration of the principles and techniques of the challenging topics. Then they solve the raised research issues by developing a series of methodologies. More specifically, the authors study the query optimization and tackle the query performance prediction for knowledge retrieval. They also handle unstructured data processing, data clustering for knowledge extraction. To optimize the queries issued through interfaces against knowledge bases, the authors propose a cache-based optimization layer between consumers and the querying interface to facilitate the querying and solve the latency issue. The cache depends on a novel learning method that considers the querying patterns from individual's historical queries without having knowledge of the backing systems of the knowledge base. To predict the query performance for appropriate query scheduling, the authors examine the queries' structural and syntactical features and apply multiple widely adopted prediction models. Their feature modelling approach eschews the knowledge requirement on both the querying languages and system. To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities. They use natural language processing techniques to pre-process the source codes and obtain the natural language elements. Then they apply traditional knowledge extraction techniques to extract knowledge. For the data from programming question-answering communities, the authors make the attempt towards building programming knowledge base by starting with paraphrase identification problems and develop novel features to accurately identify duplicate posts. For domain specific knowledge extraction, the authors propose to use a clustering technique to separate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraints in the optimization task and achieves fast and accurate performance. For each model and approach presented in this dissertation, the authors have conducted extensive experiments to evaluate it using either public dataset or synthetic data they generated.

With the growing interest in and use of big data analytics in many industries and in many research fields around the globe, this new volume addresses the need for a comprehensive resource on the core concepts of big data analytics along with the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches. The book's authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents a selection of illustrative case studies that show examples of the use of data analytics in industries such as health care, business, education, and social media. Research Practitioner's Handbook on Big Data Analytics will be a valuable addition to the libraries of practitioners in data collection in many industries along with research scholars and faculty in the domain of big data analytics. The book can also serve as a handy textbook for courses in data collection, data mining, and big data analytics.

SQL for Smarties was hailed as the first book devoted explicitly to the advanced techniques needed to transform an experienced SQL programmer into an expert. Now, 20 years later and in its fifth edition, this classic reference still reigns supreme as the only book written by a SQL master that teaches programmers and practitioners to become SQL masters themselves! These are not just tips and techniques; also offered are the best solutions to old and new challenges. Joe Celko conveys the way you need to think in order to get the most out of SQL programming efforts for both correctness and performance. New to the fifth edition, Joe features new examples to reflect the ANSI/ISO Standards so anyone can use it. He also updates data element names to meet new ISO-11179 rules with the same experience-based teaching style that made the previous editions the classics they are today. You will learn new ways to write common queries, such as finding coverings, partitions, runs in data, auctions and inventory, relational divisions and so forth. SQL for Smarties explains some of the principles of SQL programming as well as the code. A new chapter discusses design flaws in DDL, such as attribute splitting, non-normal forum redundancies and tibbling. There is a look at the traditional acid versus base transaction models, now popular in NoSQL products. You'll learn about computed columns and the DEFERRABLE options in constraints. An overview of the bi-temporal model is new to this edition and there is a longer discussion about descriptive statistic aggregate functions. The book finishes with an overview of SQL/PSM that is applicable to proprietary 4GL vendor extensions.

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

Brain Inspired Cognitive Systems - BICS 2010 aims to bring together leading scientists and engineers who use analytic and synthetic methods both to understand the astonishing processing properties of biological systems and specifically of the brain, and to exploit such knowledge to advance engineering methods to build artificial systems with higher levels of cognitive competence.

BICS is a meeting point of brain scientists and cognitive systems engineers where cross-domain ideas are fostered in the hope of getting emerging insights on the nature, operation and extractable capabilities of brains. This multiple approach is necessary because the progressively more accurate data about the brain is producing a growing need of a quantitative understanding and an associated capacity to manipulate this data and translate it into engineering applications rooted in sound theories.

BICS 2010 is intended for both researchers that aim to build brain inspired systems with higher cognitive competences, and for life scientists who use and develop mathematical and engineering approaches for a better understanding of complex biological systems like the brain.

Four major interlaced focal symposia are planned for this conference and these are organized into patterns that encourage cross-fertilization across the symposia topics. This emphasizes the role of BICS as a major meeting point for researchers and practitioners in the areas of biological and artificial cognitive systems. Debates across disciplines will enrich researchers with complementary perspectives from diverse scientific fields.

BICS 2010 will take place July 14-16, 2010, in Madrid, Spain.

Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations given in the text. Features Gets you quickly using R as a problem-solving tool Uses RStudio's integrated development environment Shows how to interface R with SQLite Includes examples using R's Rattle graphical user interface Requires no prior knowledge of R, machine learning, or computer programming Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts Includes datasets from several areas, including business, health and medicine, and science About the Author Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.

Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications presents state-of-the-art event mining approaches and applications with a focus on computing system management. The book first explains how to transform log data in disparate formats and contents into a canonical form as well as how to optimize system monitoring. It then shows how to extract useful knowledge from data. It describes intelligent and efficient methods and algorithms to perform data-driven pattern discovery and problem determination for managing complex systems. The book also discusses data-driven approaches for the detailed diagnosis of a system issue and addresses the application of event summarization in Twitter messages (tweets). Understanding the interdisciplinary field of event mining can be challenging as it requires familiarity with several research areas and the relevant literature is scattered in diverse publications. This book makes it easier to explore the field by providing both a good starting point for readers not familiar with the topics and a comprehensive reference for those already working in this area.

This book addresses the complex issue of global Internet governance by focusing on its implementation in Malaysia and Singapore. The authors draw insights, identify, revisit and flesh out the discourses circulating since the 1990s and pitch them against global internet governance concerns. Internet governance, thought managed domestically/nationally, is a global issue. It is at the heart of how the internet works yet remains hidden within the 'black box' of governance language. While several scholars have entered the fray in recent years, especially in the past decade, very few of them are aware that the Malaysian and Singaporean governments have in fact been at the forefront of Internet regulatory strategies from the early 1990s. The book identifies, revisits and gives flesh to some of the discourses circulating in Southeast Asia at the time and pitches it against current governance concerns. Readers of this book will understand how and why Malaysia and Singapore are important contributors to the issue of internet governance. This knowledge will inform a depth of understanding of why China is keenly seeking to stake its demands on internet governance and sovereignty, and likely American and global responses. Readers will also appreciate how and why the regulation of the Internet has been and will remain a site of contestation and control.

This is an overview of the end-to-end data cleaning process. Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions. Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems. This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, this book describes various error detection and repair methods, and attempts to anchor these proposals with multiple taxonomies and views. Specifically, it covers four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, it includes a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models. This book is intended to serve as a useful reference for researchers and practitioners who are interested in the area of data quality and data cleaning. It can also be used as a textbook for a graduate course. Although we aim at covering state-of-the-art algorithms and techniques, we recognize that data cleaning is still an active field of research and therefore provide future directions of research whenever appropriate.

This book is about the rise of data as a driver of innovation and economic growth. It charts the evolution of business data as a valuable resource and explores some of the key business, economic and social issues surrounding the data-driven revolution we are currently going through. Readers will gain an understanding of the historical underpinnings of the data business and why the collection and use of data has been driven by commercial needs. Readers will also gain insights into the rise of the modern data-driven technology giants, their business models and the reasons for their success. Alongside this, some of the key social issues including privacy are considered and the challenges these pose to policymakers and regulators. Finally, the impact of pervasive computing and the Internet of Things (IoT) is explored in the context of the new sources of data that are being generated. This book is useful for students and practitioners wanting to better understand the origins and drivers of the current technological revolution and the key role that data plays in innovation and business success.

This book is about the rise of data as a driver of innovation and economic growth. It charts the evolution of business data as a valuable resource and explores some of the key business, economic and social issues surrounding the data-driven revolution we are currently going through. Readers will gain an understanding of the historical underpinnings of the data business and why the collection and use of data has been driven by commercial needs. Readers will also gain insights into the rise of the modern data-driven technology giants, their business models and the reasons for their success. Alongside this, some of the key social issues including privacy are considered and the challenges these pose to policymakers and regulators. Finally, the impact of pervasive computing and the Internet of Things (IoT) is explored in the context of the new sources of data that are being generated. This book is useful for students and practitioners wanting to better understand the origins and drivers of the current technological revolution and the key role that data plays in innovation and business success.

Optimize Your Chemical Database Design and Use of Relational Databases in Chemistry helps programmers and users improve their ability to search and manipulate chemical structures and information, especially when using chemical database "cartridges". It illustrates how the organizational, data integrity, and extensibility properties of relational databases are best utilized when working with chemical information. The author facilitates an understanding of existing relational database schemas and shows how to design new schemas that contain tables of data and chemical structures. By using database extension cartridges, he provides methods to properly store and search chemical structures. He explains how to download and install a fully functioning database using free, open-source chemical extension cartridges within PostgreSQL. The author also discusses how to access a database on a computer network using both new and existing applications. Through examples of good database design, this book shows you that relational databases are the best way to store, search, and operate on chemical information.