The book aims at presenting a multidisciplinary view meant to illustrate several significant efforts and results about the contribution of information technologies to make available new resources and enable rationally usage the existing ones in the context of the ever-growing trends to use ever more resources. Authors from various countries have been invited to contribute so that a rather broad and balanced image about the current trends and recent results obtained could result. The proposed book addresses methodologies and information technology-based tools and systems designed for the efficient use of diverse resources such as manpower and human knowledge, natural resources including water, raw materials and end of life products, financial assets, datasets, and even cultural goods. The book is organized in ten chapters. It is intended to be insightful for researchers, instructors, and planers from various domains. It can also be used as an auxiliary material for postgraduate studies in applied informatics, business administration, industrial engineering, engineering and management, computer, and digital humanities.

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.

This book presents an in-depth review of the state of the art of cyber-physical systems (CPS) and their applications. Relevant case studies are also provided, to help the reader to master the interdisciplinary material. Features: includes self-test exercises in each chapter, together with a glossary; offers a variety of teaching support materials at an associated website, including a comprehensive set of slides and lecture videos; presents a brief overview of the study of systems, and embedded computing systems, before defining CPS; introduces the concepts of the Internet of Things, and ubiquitous (or pervasive) computing; reviews the design challenges of CPS, and their impact on systems and software engineering; describes the ideas behind Industry 4.0 and the revolutions in digital manufacturing, including smart and agile manufacturing, as well as cybersecurity in manufacturing; considers the social impact of the changes in skills required by the globalized, digital work environment of the future.

Military organizations around the world are normally huge producers and consumers of data. Accordingly, they stand to gain from the many benefits associated with data analytics. However, for leaders in defense organizations-either government or industry-accessible use cases are not always available. This book presents a diverse collection of cases that explore the realm of possibilities in military data analytics. These use cases explore such topics as: Context for maritime situation awareness Data analytics for electric power and energy applications Environmental data analytics in military operations Data analytics and training effectiveness evaluation Harnessing single board computers for military data analytics Analytics for military training in virtual reality environments A chapter on using single board computers explores their application in a variety of domains, including wireless sensor networks, unmanned vehicles, and cluster computing. The investigation into a process for extracting and codifying expert knowledge provides a practical and useful model for soldiers that can support diagnostics, decision making, analysis of alternatives, and myriad other analytical processes. Data analytics is seen as having a role in military learning, and a chapter in the book describes the ongoing work with the United States Army Research Laboratory to apply data analytics techniques to the design of courses, evaluation of individual and group performances, and the ability to tailor the learning experience to achieve optimal learning outcomes in a minimum amount of time. Another chapter discusses how virtual reality and analytics are transforming training of military personnel. Virtual reality and analytics are also transforming monitoring, decision making, readiness, and operations. Military Applications of Data Analytics brings together a collection of technical and application-oriented use cases. It enables decision makers and technologists to make connections between data analytics and such fields as virtual reality and cognitive science that are driving military organizations around the world forward.

This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science. Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field. In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others.

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: Presents a comprehensive study of the interdisciplinary graphical approach to NLP Covers recent computational intelligence techniques for graph-based neural network models Discusses advances in random walk-based techniques, semantic webs, and lexical networks Explores recent research into NLP for graph-based streaming data Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.

Core Data is Apple's data storage framework: it's powerful, built-in, and can integrate with iCloud. Discover all of Core Data's powerful capabilities, learn fundamental principles including thread and memory management, and add Core Data to both your iOS and OS X projects. All examples in this edition are based on Objective-C and are up-to-date for the latest versions of OS X El Capitan and iOS 9. Core Data expert Marcus Zarra walks you through a fully developed application based around the Core Data APIs. You'll build on this application throughout the book, learning key Core Data principles such as NSPredicate, NSFetchRequest, thread management, and memory management. Start with the basics of Core Data and learn how to use it to develop your application. Then delve deep into the API details. Explore how to get Core Data integrated into your application properly, and work with this flexible API to create convenience methods to improve your application's maintainability. Reduce your migration difficulties, integrate your Core Data app with iCloud and Watch Kit, and use Core Data in a queue-based environment. By the end of the book, you'll have built a full-featured application, gained a complete understanding of Core Data, and learned how to integrate your application into the iPhone/iPad platform. This third edition updates all examples for OS X El Capitan and iOS 9, and gets you up to speed on changes in multithreading and batch processing. There's a new chapter on efficiently importing data from a network location, and a new discussion of how best to pre-load data into your application. What You Need: Mac OS X El Capitan and iOS 9 and a basic working knowledge of Objective-C

Building a data-driven organization (DDO) is an enterprise-wide initiative that may consume and lock up resources for the long term. Understandably, any organization considering such an initiative would insist on a roadmap and business case to be prepared and evaluated prior to approval. This book presents a step-by-step methodology in order to create a roadmap and business case, and provides a narration of the constraints and experiences of managers who have attempted the setting up of DDOs. The emphasis is on the big decisions - the key decisions that influence 90% of business outcomes - starting from decision first and reengineering the data to the decisions process-chain and data governance, so as to ensure the right data are available at the right time, every time. Investing in artificial intelligence and data-driven decision making are now being considered a survival necessity for organizations to stay competitive. While every enterprise aspires to become 100% data-driven and every Chief Information Officer (CIO) has a budget, Gartner estimates over 80% of all analytics projects fail to deliver intended value. Most CIOs think a data-driven organization is a distant dream, especially while they are still struggling to explain the value from analytics. They know a few isolated successes, or a one-time leveraging of big data for decision making does not make an organization data-driven. As of now, there is no precise definition for data-driven organization or what qualifies an organization to call itself data-driven. Given the hype in the market for big data, analytics and AI, every CIO has a budget for analytics, but very little clarity on where to begin or how to choose and prioritize the analytics projects. Most end up investing in a visualization platform like Tableau or QlikView, which in essence is an improved version of their BI dashboard that the organization had invested into not too long ago. The most important stakeholders, the decision-makers, are rarely kept in the loop while choosing analytics projects. This book provides a fail-safe methodology for assured success in deriving intended value from investments into analytics. It is a practitioners' handbook for creating a step-by-step transformational roadmap prioritizing the big data for the big decisions, the 10% of decisions that influence 90% of business outcomes, and delivering material improvements in the quality of decisions, as well as measurable value from analytics investments. The acid test for a data-driven organization is when all the big decisions, especially top-level strategic decisions, are taken based on data and not on the collective gut feeling of the decision makers in the organization.

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.

Written primarily for database administrators who work on z/OS and who are taking the IBM DB2 11 for z/OS Database Administration certification exam (Exam 312), this resource also appeals to those who simply want to master the skills needed to be an effective database administrator of z/OS mainframes. This study guide is designed to provide those seeking certification with an intense overview of DB2 11 for z/OS and all topics covered on the exam. Sample questions are provided at the end of each chapter, along with answers and explanations.

This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics. The approach to TBIR is based on creating a thesaurus, as well as event classification and detection. N-gram thesaurus generation for query refinement offers a new method for improving the precision of retrieval, while event classification and detection approaches aid in the classification and organization of information using web documents for domain-specific retrieval applications. In turn, with regard to content based image retrieval (CBIR) the book presents a histogram construction method, which is based on human visual perceptions of color. The book's overarching goal is to introduce readers to new ideas in an easy-to-follow manner.

This book systematically reviews world Internet development over the past 50 years, and comprehensively discusses the great contributions it has made to economic and social advances. Further, it describes the development, status and trends related to the Internet in major countries around the globe in 2019, and provides an in-depth analysis of the latest conditions, dynamics and development trends in key areas, including information infrastructure, information technology, digital economy, digital government, Internet media, cyberspace security, and international cyberspace governance. Moreover, the book further modifies and enhances the Global Internet Development Index System, in order to better show the Internet development strengths and advantages in various countries, and to reflect the global development trends more comprehensively, accurately and objectively. This book reviews the significant developments and summarizes the lessons learned as well as the future challenges. From a global perspective, it offers a vision of building a community with a shared future in cyberspace based on the new concepts, new ideas and new achievements of various countries participating in cyberspace development and construction. As such it is a valuable reference resource for anyone working in Internet related fields, such as those in government departments, internet enterprises, scientific research institutions, colleges and universities wanting to fully understand world Internet development.

Cognitive Computing for Internet of Medical Things (IoMT) offers a complete assessment of the present scenario, role, challenges, technologies, and impact of IoMT-enabled smart healthcare systems. It contains chapters discussing various biomedical applications under the umbrella of the IoMT. Key Features Exploits the different prospects of cognitive computing techniques for the IoMT and smart healthcare applications Addresses the significance of IoMT and cognitive computing in the evolution of intelligent medical systems for biomedical applications Describes the different computing techniques of cognitive intelligent systems from a practical point of view: solving common life problems Explores the technologies and tools to utilize IoMT for the transformation and growth of healthcare systems Focuses on the economic, social, and environmental impact of IoMT-enabled smart healthcare systems This book is primarily aimed at graduates, researchers and academicians working in the area of development of the application of the of the application of the IoT in smart healthcare. Industry professionals will also find this book helpful.

*Covers not just the basics of storytelling with data but also the more complex issues of data searching, cleaning and scraping from a journalist's, rather than a computer scientist's, perspective. *Data Journalism courses are taught at BA and MA level on most Journalism degrees around the world, both as compulsory and elective modules. *Mike Reilley is a leading authority in this area- he founded the Journalist's Toolbox newsletter which shares digital and data tools and has over 51k followers on Twitter, so his name will draw a lot of attention to this book.

The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations. Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data. The technological advancement makes the data storage more accessible, cheaper and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structures data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry. Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience. Features The readers will get an overview about the smart manufacturing system which enables optimized manufacturing processes and benefits the users by increasing overall profit The researchers will get insight about how the big data technology leverages in finding new associations, factors and patterns through data stream observations in real time smart manufacturing systems The industrialist can get an overview about the detection of defects in design, rapid response to market, innovative products to meet the customer requirement which can benefit their per capita income in better way Discusses technical viewpoints, concepts, theories, and underlying assumptions that are used in smart manufacturing Information delivered in a user-friendly manner for students, researchers, industrial experts, and business innovators, as well as for professionals and practitioners

Provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. Provides the mathematical background to solve problems of restoring consistency and problems of integrating probabilistic knowledge bases in the integrating process. The research results presented in the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more.

This book discusses the unique nature and complexity of fog data analytics (FDA) and develops a comprehensive taxonomy abstracted into a process model. The exponential increase in sensors and smart gadgets (collectively referred as smart devices or Internet of things (IoT) devices) has generated significant amount of heterogeneous and multimodal data, known as big data. To deal with this big data, we require efficient and effective solutions, such as data mining, data analytics and reduction to be deployed at the edge of fog devices on a cloud. Current research and development efforts generally focus on big data analytics and overlook the difficulty of facilitating fog data analytics (FDA). This book presents a model that addresses various research challenges, such as accessibility, scalability, fog nodes communication, nodal collaboration, heterogeneity, reliability, and quality of service (QoS) requirements, and includes case studies demonstrating its implementation. Focusing on FDA in IoT and requirements related to Industry 4.0, it also covers all aspects required to manage the complexity of FDA for IoT applications and also develops a comprehensive taxonomy.

Navigation Design and SEO for Content-Intensive Websites: A Guide for an Efficient Digital Communication presents the characteristics and principal guidelines for the analysis and design of efficient navigation and information access systems on content-intensive websites, such as magazines and other media publications. Furthermore, the book aims to present the tools of information processing, including information architecture (IA) and content categorization systems, so that such designs can ensure a good navigation experience based on the semantic relations between content items. The book also presents best practices in the design of information access systems with regard to their main structures, including search query forms and search result pages. Finally, the book describes the foundations of search engine optimization (SEO), emphasizing SEO oriented to publications focused on communication and the coverage of current affairs, including images and videos.

The information infrastructure - comprising computers, embedded devices, networks and software systems - is vital to operations in every sector: chemicals, commercial facilities, communications, critical manufacturing, dams, defense industrial base, emergency services, energy, financial services, food and agriculture, government facilities, healthcare and public health, information technology, nuclear reactors, materials and waste, transportation systems, and water and wastewater systems. Global business and industry, governments, indeed society itself, cannot function if major components of the critical information infrastructure are degraded, disabled or destroyed.Critical Infrastructure Protection XIV describes original research results and innovative applications in the interdisciplinary field of critical infrastructure protection. Also, it highlights the importance of weaving science, technology and policy in crafting sophisticated, yet practical, solutions that will help secure information, computer and network assets in the various critical infrastructure sectors. Areas of coverage include: Aviation Infrastructure Security; Vehicle Infrastructure Security; Telecommunications Systems Security; Industrial Control Systems Security; Cyber-Physical Systems Security; and Infrastructure Modeling and Simulation. This book is the fourteenth volume in the annual series produced by the International Federation for Information Processing (IFIP) Working Group 11.10 on Critical Infrastructure Protection, an international community of scientists, engineers, practitioners and policy makers dedicated to advancing research, development and implementation efforts focused on infrastructure protection. The book contains a selection of sixteen edited papers from the Fourteenth Annual IFIP WG 11.10 International Conference on Critical Infrastructure Protection, held at SRI International, Arlington, Virginia, USA in the spring of 2020. Critical Infrastructure Protection XIV is an important resource for researchers, faculty members and graduate students, as well as for policy makers, practitioners and other individuals with interests in homeland security.

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data's full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.

Mashups are mostly lightweight Web applications that offer new functionalities by combining, aggregating and transforming resources and services available on the Web. Popular examples include a map in their main offer, for instance for real estate, hotel recommendations, or navigation tools. Mashups may contain and mix client-side and server-side activity. Obviously, understanding the incoming resources (services, statistical figures, text, videos, etc.) is a precondition for optimally combining them, so that there is always some undercover semantics being used. By using semantic annotations, neutral mashups permute into the branded type of semantic mashups. Further and deeper semantic processing such as reasoning is the next step. The chapters of this book reflect the diversity of real-life semantic mashups. Two overview chapters take the reader to the environments where mashups are at home and review the regulations (standards, guidelines etc.) mashups are based on and confronted with. Chapters focusing on DBpedia, search engines and the Web of Things inspect the main Web surroundings of mashups. While mashups upgrading search queries may be nearer to the everyday experience of readers, mashups using DBpedia input and sensor data from the real world lead to important new and therefore less known developments. Finally, the diversity of mashups is tracked through a few application areas: mathematical knowledge, speech, crisis and disaster management, recommendations (for games), inner-city information, and tourism. Participants of the AI Mashup Challenge wrote all the chapters of this book. The authors were writing for their current and future colleagues - researchers and developers all over the Web who integrate mashup functionalities into their thinking and possibly into their applications.

This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

What is the uniquely human factor in finding and using information to produce new knowledge? Is there an underlying aspect of our thinking that cannot be imitated by the AI-equipped machines that will increasingly dominate our lives? This book answers these questions, and tells us about our consciousness - its drive or intention in seeking information in the world around us, and how we are able to construct new knowledge from this information. The book is divided into three parts, each with an introduction and a conclusion that relate the theories and models presented to the real-world experience of someone using a search engine. First, Part I defines the exceptionality of human consciousness and its need for new information and how, uniquely among all other species, we frame our interactions with the world. Part II then investigates the problem of finding our real information need during information searches, and how our exceptional ability to frame our interactions with the world blocks us from finding the information we really need. Lastly, Part III details the solution to this framing problem and its operational implications for search engine design for everyone whose objective is the production of new knowledge. In this book, Charles Cole deliberately writes in a conversational style for a broader readership, keeping references to research material to the bare minimum. Replicating the structure of a detective novel, he builds his arguments towards a climax at the end of the book. For our video-game, video-on-demand times, he has visualized the ideas that form the book's thesis in over 90 original diagrams. And above all, he establishes a link between information need and knowledge production in evolutionary psychology, and thus bases his arguments in our origins as a species: how we humans naturally think, and how we naturally search for new information because our consciousness drives us to need it.

Database Semantics: Semantic Issues in Multimedia Systems reflects the state of the art of emerging research on the meaning of multimedia information, as presented during IFIP's Eighth Data Semantics Working Conference (DS-8), organized by its Working Group 2.6 on Databases, and held at Rotorua, New Zealand, in January 1999. DS-8 was planned as an active forum for researchers and practitioners focusing on those issues that involve the semantics of the information represented, stored, and manipulated by multimedia systems. Depending on the topic and state of research, issues may be covered either deeply theoretically or quite practically, or even both. These proceedings contain twenty-one papers carefully selected by an International Programme Committee and organized in six thematic areas: Video Data Modelling and Use; Image Databases; Applications of Multimedia Systems; Multimedia Modeling in General; Multimedia Information Retrieval; Semantics and Metadata. For almost every area, important topics and issues include: data modeling and query languages for media such as audio, video, and images; methodological aspects of multimedia database design; intelligent multimedia information retrieval; knowledge discovery and data mining in multimedia information; multimedia user interfaces. Three visionary keynote addresses, by famous experts Ramesh Jain, Hermann Maurer and Masao Sakauchi, set the stage for discussion and future directions for the field. The collection of papers that resulted now offers a glimpse of the excitement and enthusiasm from DS-8. Database Semantics: Semantic Issues in Multimedia Systems is suitable as a secondary text for a graduate-level course on database systems, multimedia systems, or information retrieval systems and as a reference for practitioners and researchers in industry.

The book IoT and Big Data Analytics (IoT-BDA) for Smart Cities - A Global Perspective, emphasizes the challenges, architectural models, and intelligent frameworks with smart decisionmaking systems using Big Data and IoT with case studies. The book illustrates the benefits of Big Data and IoT methods in framing smart systems for smart applications. The text is a coordinated amalgamation of research contributions and industrial applications in the field of smart cities. Features: Provides the necessity of convergence of Big Data Analytics and IoT techniques in smart city application Challenges and Roles of IoT and Big Data in Smart City applications Provides Big Data-IoT intelligent smart systems in a global perspective Provides a predictive framework that can handle the traffic on abnormal days, such as weekends and festival holidays Gives various solutions and ideas for smart traffic development in smart cities Gives a brief idea of the available algorithms/techniques of Big Data and IoT and guides in developing a solution for smart city applications This book is primarily aimed at IT professionals. Undergraduates, graduates, and researchers in the area of computer science and information technology will also find this book useful.