This book highlights recent research advances on biometrics using new methods such as deep learning, nonlinear graph embedding, fuzzy approaches, and ensemble learning. Included are special biometric technologies related to privacy and security issues, such as cancellable biometrics and soft biometrics. The book also focuses on several emerging topics such as big data issues, internet of things, medical biometrics, healthcare, and robot-human interactions. The authors show how these new applications have triggered a number of new biometric approaches. They show, as an example, how fuzzy extractor has become a useful tool for key generation in biometric banking, and vein/heart rates from medical records can also be used to identify patients. The contributors cover the topics, their methods, and their applications in depth.

This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms. The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained. Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system's utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph. This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems.

This book describes how the creation of new digital services-through vertical and horizontal integration of data coming from sensors on top of existing legacy systems-that has already had a major impact on industry is now extending to healthcare. The book describes the fourth industrial revolution (i.e. Health 4.0), which is based on virtualization and service aggregation. It shows how sensors, embedded systems, and cyber-physical systems are fundamentally changing the way industrial processes work, their business models, and how we consume, while also affecting the health and care domains. Chapters describe the technology behind the shift of point of care to point of need and away from hospitals and institutions; how care will be delivered virtually outside hospitals; that services will be tailored to individuals rather than being designed as statistical averages; that data analytics will be used to help patients to manage their chronic conditions with help of smart devices; and that pharmaceuticals will be interactive to help prevent adverse reactions. The topics presented will have an impact on a variety of healthcare stakeholders in a continuously global and hyper-connected world. * Presents explanations of emerging topics as they relate to e-health, such as Industry 4.0, Precision Medicine, Mobile Health, 5G, Big Data, and Cyber-physical systems; * Provides overviews of technologies in addition to possible application scenarios and market conditions; * Features comprehensive demographic and statistic coverage of Health 4.0 presented in a graphical manner.

This book presents real-world decision support systems, i.e., systems that have been running for some time and as such have been tested in real environments and complex situations; the cases are from various application domains and highlight the best practices in each stage of the system's life cycle, from the initial requirements analysis and design phases to the final stages of the project. Each chapter provides decision-makers with recommendations and insights into lessons learned so that failures can be avoided and successes repeated. For this reason unsuccessful cases, which at some point of their life cycle were deemed as failures for one reason or another, are also included. All decision support systems are presented in a constructive, coherent and deductive manner to enhance the learning effect. It complements the many works that focus on theoretical aspects or individual module design and development by offering 'good' and 'bad' practices when developing and using decision support systems. Combining high-quality research with real-world implementations, it is of interest to researchers and professionals in industry alike.

Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a 'thought process' that is capable of learning abstract concepts built from simpler primitives. These models are especially useful for image processing applications. At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download. What You Will Learn Discover convolutional nets and how to use them Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs Master the various programming algorithms required Carry out multi-threaded gradient computations and memory allocations for this threading Work with CUDA code implementations of all core computations, including layer activations and gradient calculations Make use of the CONVNET program and manual to explore convolutional nets and case studies Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

This book offers an overview of knowledge management. It starts with an introduction to the subject, placing descriptive models in the context of the overall field as well as within the more specific field of data mining analysis. Chapter 2 covers data visualization, including directions for accessing R open source software (described through Rattle). Both R and Rattle are free to students. Chapter 3 then describes market basket analysis, comparing it with more advanced models, and addresses the concept of lift. Subsequently, Chapter 4 describes smarketing RFM models and compares it with more advanced predictive models. Next, Chapter 5 describes association rules, including the APriori algorithm and provides software support from R. Chapter 6 covers cluster analysis, including software support from R (Rattle), KNIME, and WEKA, all of which are open source. Chapter 7 goes on to describe link analysis, social network metrics, and open source NodeXL software, and demonstrates link analysis application using PolyAnalyst output. Chapter 8 concludes the monograph. Using business-related data to demonstrate models, this descriptive book explains how methods work with some citations, but without detailed references. The data sets and software selected are widely available and can easily be accessed.

This guide is for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance. This is the first of a two-part guide to SPSS for Windows, introducing data entry into SPSS, along with elementary statistical and graphical methods for summarizing and presenting data. Part I also covers the rudiments of hypothesis testing and business forecasting while Part II will present multivariate statistical methods, more advanced forecasting methods, and multivariate methods. IBM SPSS Statistics offers a powerful set of statistical and information analysis systems that run on a wide variety of personal computers. The software is built around routines that have been developed, tested, and widely used for more than 20 years. As such, IBM SPSS Statistics is extensively used in industry, commerce, banking, local and national governments, and education. Just a small subset of users of the package include the major clearing banks, the BBC, British Gas, British Airways, British Telecom, the Consumer Association, Eurotunnel, GSK, TfL, the NHS, Shell, Unilever, and W.H.S. Although the emphasis in this guide is on applications of IBM SPSS Statistics, there is a need for users to be aware of the statistical assumptions and rationales underpinning correct and meaningful application of the techniques available in the package; therefore, such assumptions are discussed, and methods of assessing their validity are described. Also presented is the logic underlying the computation of the more commonly used test statistics in the area of hypothesis testing. Mathematical background is kept to a minimum.

Causal analytics methods can revolutionize the use of data to make effective decisions by revealing how different choices affect probabilities of various outcomes. This book presents and illustrates models, algorithms, principles, and software for deriving causal models from data and for using them to optimize decisions with uncertain outcomes. It discusses how to describe and summarize situations; detect changes; evaluate effects of policies or interventions; learn what works best under different conditions; predict values of as-yet unobserved quantities from available data; and identify the most likely explanations for observed outcomes, including surprises and anomalies. The book resents practical techniques for causal modeling and analytics that practitioners can apply to improve understanding of how choices affect probabilities of consequences and, based on this understanding, to recommend choices that are more likely to accomplish their intended objectives.The book begins with a survey of modern analytics methods, focusing mainly on techniques useful for decision, risk, and policy analysis. Chapter 2 introduces free in-browser software, including the Causal Analytics Toolkit (CAT) software, to enable readers to perform the analyses described and to apply modern analytics methods easily to their own data sets. Chapters 3 through 11 show how to apply causal analytics and risk analytics to practical risk analysis challenges, mainly related to public and occupational health risks from pathogens in food or from pollutants in air. Chapters 12 through 15 turn to broader questions of how to improve risk management decision-making by individuals, groups, organizations, institutions, and multi-generation societies with different cultures and norms for cooperation. These chapters examine organizational learning, community resilience, societal risk management, and intergenerational collaboration and justice in managing risks.

This book explores the factors that make digital disruption possible and the effects this has on existing business models. It takes a look at the industries that are most susceptible to disruption and highlights what executives can do to take advantage of disruption to re-invent their business model. It also examines the pivotal role that technology plays in creating new dynamics to business operations and forcing business model changes. Adoption of digital technology has caused process disruptions in a number of industries and led to new business models (e.g., UEber, AirBnb) and new products. In addition to covering some of the more popular and well known examples, this book targets not so obvious disruptions in the education sector and in services and changing business models. Phantom Ex Machina: Digital Disruption's Role in Business Model Transformation is divided into six parts. The book begins with an introduction to digital disruption and why it matters. The next part of the book focuses on business strategy which includes case studies on the impact of social media and how digital disruption changes pricing strategies and price models. For part three, the authors observe technology's role in digital disruptions. Chapters cover how 3D printing is challenging existing business models and how the automotive industry is innovating with new perspectives. Part four covers higher education, recognizing digital disruption's transformation in graduate management education. Part five centers upon the service industry with a look at virtual teams and the emergence of virtual think tanks. Finally the book concludes with a look to the future, embracing disruptions.

This book reports on cutting-edge research on social and occupational ergonomics, presenting innovative contributions to the optimization of sociotechnical management systems related to organizational, policy, and logistical issues. It discusses timely topics related to communication, crew resource management, work design, participatory design, as well as teamwork, community ergonomics, cooperative work, and warning systems, and explores new work paradigms, organizational cultures, virtual organizations, telework, and quality management. The book also describes pioneering infrastructures implemented for different purposes such as urban, health, and enterprise, and examines the changing role of automated systems, offering innovative solutions that address the needs of particular populations. Based on the AHFE 2018 International Conference on Social and Occupational Ergonomics, held in Orlando, Florida, USA on July 21-25, 2018, the book provides readers with a comprehensive overview of the current challenges in both organizational and occupational ergonomics, highlighting key connections between them and underlining the importance of emotional factors in influencing human performance.

Drawing on research conducted over the last 3 years in Cornwall, UK, this new book explores the impact of the EU/BT funded introduction of fibre broadband on rural SMEs. Including a qualitative methodology and in depth focus groups with over 200 companies, The Impact of Fibre Connectivity on SMEs provides a detailed, in depth, analysis to challenge conventionally held beliefs in the value of subsidy and policy pressure in the deployment of such projects. With focus on regional development in the UK as well as exploration of the wider EU context, the book presents a genuine insight into the technology adoption and technology provision.

The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data's immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data. The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments.

This book offers a multidisciplinary resource on digital government, while specifically focusing on its role within the emerging market of India. The Government of India (GoI) is concentrating on transforming India under the Digital India initiative. In order to do so, it has emphasized three core areas: (1) Computing infrastructure as a utility to every citizen; (2) Governance and services on demand; and (3) Digital empowerment of citizens. The chapters in this book address issues surrounding these areas, highlighting concepts such as knowledge societies, urban operations and logistics, issues in managing emergent Information Communication Technologies (ICTs), and also smart analytics for urbanization. The chapters contribute to the theory, practice and policy for a "Digital India." The book captures lessons, knowledge, experiences (about challenges, drivers, antecedents, etc.) and best practices emerging from implementation of various projects. While the book is dedicated to a "Digital India," this book can also be valuable resource for public administrators, government officials and researchers in other emerging markets and developing countries in Asia, Africa and Latin America where similar socio-political and economic conditions exist.

This book presents cutting edge research on the development of analytics in travel and tourism. It introduces new conceptual frameworks and measurement tools, as well as applications and case studies for destination marketing and management. It is divided into five parts: Part one on travel demand analytics focuses on conceptualizing and implementing travel demand modeling using big data. It illustrates new ways to identify, generate and utilize large quantities of data in tourism demand forecasting and modeling. Part two focuses on analytics in travel and everyday life, presenting recent developments in wearable computers and physiological measurement devices, and the implications for our understanding of on-the-go travelers and tourism design. Part three embraces tourism geoanalytics, correlating social media and geo-based data with tourism statistics. Part four discusses web-based and social media analytics and presents the latest developments in utilizing user-generated content on the Internet to understand a number of managerial problems. The final part is a collection of case studies using web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging online reviews in the hotel industry, and evaluating destination communications and market intelligence with online hotel reviews. The chapters in this section collectively describe a range of different approaches to understanding market dynamics in tourism and hospitality.

This book focuses on the implementation of digital strategies in the public sectors in the US, Mexico, Brazil, India and Germany. The case studies presented examine different digital projects by looking at their impact as well as their alignment with their national governments' digital strategies. The contributors assess the current state of digital government, analyze the contribution of digital technologies in achieving outcomes for citizens, discuss ways to measure digitalization and address the question of how governments oversee the legal and regulatory obligations of information technology. The book argues that most countries formulate good strategies for digital government, but do not effectively prescribe and implement corresponding policies and programs. Showing specific programs that deliver results can help policy makers, knowledge specialists and public-sector researchers to develop best practices for future national strategies.

This textbook is intended for an introductory graduate level on process control, taught in most engineering curricula. It focuses on the statistical techniques and methods of control and system optimization needed for the mathematical modeling, analysis, simulation, control and optimization of multivariable manufacturing processes. In four sections, it covers: Relevant mathematical methods, including random events, variables and processes, and their characteristics; estimation and confidence intervals; Bayes applications; correlation and regression analysis; statistical cluster analysis; and singular value decomposition for classification applications. Mathematical description of manufacturing processes, including static and dynamic models; model validation; confidence intervals for model parameters; principal component analysis; conventional and recursive least squares procedures; nonlinear least squares; and continuous-time, discrete-time, s-domain and Z-domain models. Control of manufacturing processes, including transfer function/transfer matrix models; state-variable models; methods of discrete-time classical control; state variable discrete-time control; state observers/estimators in control systems; methods of decoupling control; and methods of adaptive control. Methods and applications of system optimization, including unconstrained and constrained optimization; analytical and numerical optimization procedures; use of penalty functions; methods of linear programming; gradient methods; direct search methods; genetic optimization; methods and applications of dynamic programming; and applications to estimation, design, control, and planning. Each section of the book will include end-of-chapter exercises, and the book will be suitable for any systems, electrical, chemical, or industrial engineering program, as it focuses on the processes themselves, and not on the product being manufactured. Students will be able to obtain a mathematical model of any manufacturing process, to design a computer-based control system for a particular continuous manufacturing process, and be able to formulate an engineering problem in terms of optimization, as well as the ability to choose and apply the appropriate optimization technique.

This is the first book to explore how Semantic Web technologies (SWTs) can be used to create intelligent engineering applications (IEAs). Technology-specific chapters reflect the state of the art in relevant SWTs and offer guidelines on how they can be applied in multi-disciplinary engineering settings characteristic of engineering production systems. In addition, a selection of case studies from various engineering domains demonstrate how SWTs can be used to create IEAs that enable, for example, defect detection or constraint checking. Part I "Background and Requirements of Industrie 4.0 for Semantic Web Solutions" provides the background information needed to understand the book and addresses questions concerning the semantic challenges and requirements of Industrie 4.0, and which key SWT capabilities may be suitable for implementing engineering applications. In turn, Part II "Semantic Web-Enabled Data Integration in Multi-Disciplinary Engineering" focuses on how SWTs can be used for data integration in heterogeneous, multi-disciplinary engineering settings typically encountered in the creation of flexible production systems. Part III "Creating Intelligent Applications for Multi-Disciplinary Engineering" demonstrates how the integrated engineering data can be used to support the creation of IEAs, while Part IV "Related and Emerging Trends in the Use of Semantic Web in Engineering" presents an overview of the broader spectrum of approaches that make use of SWTs to support engineering settings. A final chapter then rounds out the book with an assessment of the strengths, weaknesses and compatibilities of SWTs and an outlook on future opportunities for applying SWTs to create IEAs in flexible industrial production systems. This book seeks to build a bridge between two communities: industrial production on one hand and Semantic Web on the other. Accordingly, stakeholders from both communities should find this book useful in their work. Semantic Web researchers will gain a better understanding of the challenges and requirements of the industrial production domain, offering them guidance in the development of new technologies and solutions for this important application area. In turn, engineers and managers from engineering domains will arrive at a firmer grasp of the benefits and limitations of using SWTs, helping them to select and adopt appropriate SWTs more effectively. In addition, researchers and students interested in industrial production-related issues will gain valuable insights into how and to what extent SWTs can help to address those issues.

This book reports on the latest research and developments in the field of cybersecurity, particularly focusing on personal security and new methods for reducing human error and increasing cyber awareness, as well as innovative solutions for increasing the security of advanced Information Technology (IT) infrastructures. It covers a broad range of topics, including methods for human training; novel cyber-physical and process-control systems; social, economic, and behavioral aspects of cyberspace; issues concerning the cybersecurity index; security metrics for enterprises; and risk evaluation. Based on the AHFE 2018 International Conference on Human Factors in Cybersecurity, held on July 21-25, 2018, in Orlando, Florida, USA, the book not only presents innovative cybersecurity technologies, but also discusses emerging threats, current gaps in the available systems, and future challenges that can be successfully overcome with the help of human factors research.

This book discusses the effective use of modern ICT solutions for business needs, including the efficient use of IT resources, decision support systems, business intelligence, data mining and advanced data processing algorithms, as well as the processing of large datasets (inter alia social networking such as Twitter and Facebook, etc.). The ability to generate, record and process qualitative and quantitative data, including in the area of big data, the Internet of Things (IoT) and cloud computing offers a real prospect of significant improvements for business, as well as the operation of a company within Industry 4.0. The book presents new ideas, approaches, solutions and algorithms in the area of knowledge representation, management and processing, quantitative and qualitative data processing (including sentiment analysis), problems of simulation performance, and the use of advanced signal processing to increase the speed of computation. The solutions presented are also aimed at the effective use of business process modeling and notation (BPMN), business process semantization and investment project portfolio selection. It is a valuable resource for researchers, data analysts, entrepreneurs and IT professionals alike, and the research findings presented make it possible to reduce costs, increase the accuracy of investment, optimize resources and streamline operations and marketing.

This classroom-texted textbook/reference presents a set of useful modeling techniques, describing how these can be combined into a powerful framework for the analysis and design of business systems. These techniques follow an interactive modeling and simulation (IMS) approach, enabling the modeling and simulation of separate parts of the system at different levels of abstraction, and the composition of these parts in a flexible crosscutting manner that preserves the behavior of the individual parts. Topics and features: presents a detailed introduction to the foundations of IMS for business system design, covering protocol modeling and goal modeling semantics; describes the practical application of IMS for business system design, illustrated by a selection of useful case studies; highlights the advantages of this approach to IMS for business system design, with a focus on performance management, motivation modeling, and communication; includes review questions and exercises at the end of each chapter.

This book covers the whole spectrum of modeling goals to achieve optimal quality in the process model developed. It focuses on how to balance quality considerations across all semiotic levels when models are used for different purposes, and is based on SEQUAL, a framework for understanding the quality of models and modeling languages, which can take into account all main aspects relating to the quality of models. Chapter 1 focuses on the theoretical foundations, introducing readers to the topics of business processes and business process modeling, as well as the most important concept underlying the modeling of business processes. In turn, Chapter 2 addresses the quality of models in general and business process models in particular. Chapter 3 contains a specialization of SEQUAL for quality of business process models. In Chapter 4, examples of the practical uses of business process models are provided, together with the results of detailed case studies on how to achieve and maintain quality in business process models. Chapter 5 presents a process modeling value framework that demonstrates how to achieve more long-term and higher return on investment with regard to (business) process and enterprise models. Lastly, Chapter 6 reviews the main points of the book and discusses the potential for business process modeling in the future through its combination with other types of modeling. The book has two intended audiences. It is primarily intended for computer science, software engineering and information system students at the postgraduate level who want to know more about business process modeling and the quality of models in preparation for professional practice. The second audience consists of professionals with extensive experience in and responsibilities related to the development and evolution of process-oriented information systems and information systems methodologies in general, who need to formalize and structure their practical experience or update their knowledge as a way to improve their professional activity. The book also includes a number of real-world case studies that make it easier to grasp the main theoretical concepts, helping readers apply the approaches described.

This book constitutes extended and revised papers from the 19th International Conference on Enterprise Information Systems, ICEIS 2017, held in Porto, Portugal, in April 2017. The 28 papers presented in this volume were carefully reviewed and selected for inclusion in this book from a total of 318 submissions. They were organized in topical sections named: databases and information systems integration; artificial intelligence and decision support systems; information systems analysis and specification; software agents and internet computing; human-computer interaction; and enterprise architecture.

This book constitutes the proceedings of the 21st International Conference on Business Information Systems, BIS 2018, held in Berlin, Germany, in July 2018. The BIS conference follows popular research trends, both in the academic and the business domain. Thus the theme of BIS 2018 was "Digital Transformation - An Imperative in Today's Business Markets". The 30 papers presented in this volume were carefully reviewed and selected from 96 submissions. They were organized in topical sections named: big and smart data and artificial intelligence; business and enterprise modeling; ICT project management; process management; smart infrastructures; social media and Web-based business information systems; applications, evaluations, and experiences.

This book offers a selection of the best papers presented at the 13th International Symposium on Location Based Services (LBS 2016), which was held in Vienna (Austria) from November 14 to 16, 2016. It provides an overview of recent research in the field, including the latest advances in outdoor/indoor positioning, smart environment, spatial modeling, personalization and context awareness, cartographic communication, novel user interfaces, crowd sourcing, social media, big data analysis, usability and privacy.

This proceedings volume presents the latest research from the worldwide mass customization & personalization (MCP) community bringing together new thoughts and results from various disciplines within the field. The chapters are based on papers from the MCPC 2017. The book showcases research and practice from authors that see MCP as an opportunity to extend or even revolutionize current business models. The current trends of Industrie 4.0, digital manufacturing, and the rise of smart products allow for a fresh perspective on MCP: Customization 4.0. The book places a new set of values in the centre of the debate: a world with finite resources, global population growth, and exacerbating climate change needs smart thinking to engage the most effective capabilities and resources. It discusses how Customization 4.0 fosters sustainable development and creates shared value for companies, customers, consumers, and the society as a whole. The chapters of this book are contributed by a wide range of specialists, offering cutting-edge research, as well as insightful advances in industrial practice in key areas. The MCPC 2017 has a strong focus on real life MCP applications, and this proceedings volume reflects this. MCP strategies aim to profit from the fact that people are different. Their objective is to turn customer heterogeneities into opportunities, hence addressing "long tail" business models. The objective of MCP is to provide goods and services that best serve individual customers' needs with near mass production efficiency. This proceedings volume highlights the interdisciplinary work of thought leaders, technology developers, and researchers with corporate entrepreneurs putting these strategies into practice. Chapter 24 is open access under a CC BY 4.0 license via link.springer.com.