This book presents different use cases in big data applications and related practical experiences. Many businesses today are increasingly interested in utilizing big data technologies for supporting their business intelligence so that it is becoming more and more important to understand the various practical issues from different practical use cases. This book provides clear proof that big data technologies are playing an ever increasing important and critical role in a new cross-discipline research between computer science and business.

Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.

The issue of missing data imputation has been extensively explored in information engineering, though needing a new focus and approach in research. Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques focuses on methods to estimate missing values given to observed data. Providing a defining body of research valuable to those involved in the field of study, this book presents current and new computational intelligence techniques that allow computers to learn the underlying structure of data.

Data warehouses have captured the attention of practitioners and researchers alike. But the design and optimization of data warehouses remains an art rather than a science. This book presents the first comparative review of the state of the art and best current practice of data warehouses. It covers source and data integration, multidimensional aggregation, query optimization, update propagation, metadata management, quality assessment, and design optimization. Also, based on results of the European Data Warehouse Quality project, it offers a conceptual framework by which the architecture and quality of data warehouse efforts can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modeling, and artificial intelligence. For researchers and database professionals in academia and industry, the book offers an excellent introduction to the issues of quality and metadata usage in the context of data warehouses.

Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.

Web Intelligence is a new direction for scientific research and development that explores the fundamental roles as well as practical impacts of artificial intelligence and advanced information technology for the next generation of Web-empowered systems, services, and environments. Web Intelligence is regarded as the key research field for the development of the Wisdom Web (including the Semantic Web). As the first book devoted to Web Intelligence, this coherently written multi-author monograph provides a thorough introduction and a systematic overview of this new field. It presents both the current state of research and development as well as application aspects. The book will be a valuable and lasting source of reference for researchers and developers interested in Web Intelligence. Students and developers will additionally appreciate the numerous illustrations and examples.

This book offers a unique review of how astronomical information handling (in the broad sense) evolved in the course of the 20th century, and especially during its second half. This volume is a natural complement to the book Information handling in astronomy published in the same series. The scope of these two volumes includes not only dealing with professional astronomical data from the collecting instruments (ground-based and space-borne) to the users/researchers, but also publishing, education and public outreach. In short, the information flow in astronomy is thus illustrated from sources (cosmic objects) to end (mankind's knowledge). The experts contributing to this book have done their best to write in a way understandable to readers not necessarily hyperspecialized in astronomy while providing specific detailed information, as well as plenty of pointers and bibliographic elements. Especially enlightening are some lessons learned' sections.

Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions, such as using the credit card or phone, result in automated data storage, which brings us to a fairly new topic called data streams. This volume covers mining aspects of data streams in a comprehensive style, in which each contributed chapter contains a survey on the topic, the key ideas in the field from that particular topic, and future research directions. Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for graduate-level students in computer science.

The ethics of data and analytics, in many ways, is no different than any endeavor to find the "right" answer. When a business chooses a supplier, funds a new product, or hires an employee, managers are making decisions with moral implications. The decisions in business, like all decisions, have a moral component in that people can benefit or be harmed, rules are followed or broken, people are treated fairly or not, and rights are enabled or diminished. However, data analytics introduces wrinkles or moral hurdles in how to think about ethics. Questions of accountability, privacy, surveillance, bias, and power stretch standard tools to examine whether a decision is good, ethical, or just. Dealing with these questions requires different frameworks to understand what is wrong and what could be better. Ethics of Data and Analytics: Concepts and Cases does not search for a new, different answer or to ban all technology in favor of human decision-making. The text takes a more skeptical, ironic approach to current answers and concepts while identifying and having solidarity with others. Applying this to the endeavor to understand the ethics of data and analytics, the text emphasizes finding multiple ethical approaches as ways to engage with current problems to find better solutions rather than prioritizing one set of concepts or theories. The book works through cases to understand those marginalized by data analytics programs as well as those empowered by them. Three themes run throughout the book. First, data analytics programs are value-laden in that technologies create moral consequences, reinforce or undercut ethical principles, and enable or diminish rights and dignity. This places an additional focus on the role of developers in their incorporation of values in the design of data analytics programs. Second, design is critical. In the majority of the cases examined, the purpose is to improve the design and development of data analytics programs. Third, data analytics, artificial intelligence, and machine learning are about power. The discussion of power-who has it, who gets to keep it, and who is marginalized-weaves throughout the chapters, theories, and cases. In discussing ethical frameworks, the text focuses on critical theories that question power structures and default assumptions and seek to emancipate the marginalized.

Astronomical photographs contain an enormous amount of information. This presents extremely interesting problems when one wishes to produce digitized sky atlases, to archive the digitized material, to develop sophisticated devices to do the digitizing, and to create software to process the vast amounts of data. All these activities are necessary to be able to carry out astronomy work. One such activity is the important, large-scale optical identification of objects which also emit radiation at other wavelengths. Other activities of the past decade include a multiplicity of surveys that have been made on galaxies and clusters of galaxies. This book treats, in five sections, the existing and future surveys, their digitization and their impact on astronomy. It is designed to serve as a reference for people in the field and for those who wish to engage in using or producing sky surveys.

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

"A Computer Science Reader" covers the entire field of computing, from its technological status through its social, economic and political significance. The book's clearly written selections represent the best of what has been published in the first three-and-a-half years of "ABACUS," Springer-Verlag's internatioanl quarterly journal for computing professionals. Among the articles included are: - U.S. versus IBM: An Exercise in Futility? by Robert P. Bigelow - Programmers: The Amateur vs. the Professional by Henry Ledgard - The Composer and the Computer by Lejaren Hiller - SDI: A Violation of Professional Responsibility by David L. Parnas - Who Invented the First Electronic Digital Computer? by Nancy Stern - Foretelling the Future by Adaptive Modeling by Ian H. Witten and John G. Cleary - The Fifth Generation: Banzai or Pie-in-the-Sky? by Eric A. Weiss This volume contains more than 30 contributions by outstanding and authoritative authors grouped into the magazine's regular categories: Editorials, Articles, Departments, Reports from Correspondents, and Features. "A" "Computer Science Reader" will be interesting and important to any computing professional or student who wants to know about the status, trends, and controversies in computer science today.

Multiple processor systems are an important class of parallel systems. Over the years, several architectures have been proposed to build such systems to satisfy the requirements of high performance computing. These architectures span a wide variety of system types. At the low end of the spectrum, we can build a small, shared-memory parallel system with tens of processors. These systems typically use a bus to interconnect the processors and memory. Such systems, for example, are becoming commonplace in high-performance graph ics workstations. These systems are called uniform memory access (UMA) multiprocessors because they provide uniform access of memory to all pro cessors. These systems provide a single address space, which is preferred by programmers. This architecture, however, cannot be extended even to medium systems with hundreds of processors due to bus bandwidth limitations. To scale systems to medium range i. e., to hundreds of processors, non-bus interconnection networks have been proposed. These systems, for example, use a multistage dynamic interconnection network. Such systems also provide global, shared memory like the UMA systems. However, they introduce local and remote memories, which lead to non-uniform memory access (NUMA) architecture. Distributed-memory architecture is used for systems with thousands of pro cessors. These systems differ from the shared-memory architectures in that there is no globally accessible shared memory. Instead, they use message pass ing to facilitate communication among the processors. As a result, they do not provide single address space."

This book thoroughly covers the remote sensing visualization and analysis techniques based on computational imaging and vision in Earth science. Remote sensing is considered a significant information source for monitoring and mapping natural and man-made land through the development of sensor resolutions that committed different Earth observation platforms. The book includes related topics for the different systems, models, and approaches used in the visualization of remote sensing images. It offers flexible and sophisticated solutions for removing uncertainty from the satellite data. It introduces real time big data analytics to derive intelligence systems in enterprise earth science applications. Furthermore, the book integrates statistical concepts with computer-based geographic information systems (GIS). It focuses on image processing techniques for observing data together with uncertainty information raised by spectral, spatial, and positional accuracy of GPS data. The book addresses several advanced improvement models to guide the engineers in developing different remote sensing visualization and analysis schemes. Highlights on the advanced improvement models of the supervised/unsupervised classification algorithms, support vector machines, artificial neural networks, fuzzy logic, decision-making algorithms, and Time Series Model and Forecasting are addressed. This book guides engineers, designers, and researchers to exploit the intrinsic design remote sensing systems. The book gathers remarkable material from an international experts' panel to guide the readers during the development of earth big data analytics and their challenges.

The authors focus on the mathematical models and methods that support most data mining applications and solution techniques.

The most important use of computing in the future will be in the context of the global "digital convergence" where everything becomes digital and every thing is inter-networked. The application will be dominated by storage, search, retrieval, analysis, exchange and updating of information in a wide variety of forms. Heavy demands will be placed on systems by many simultaneous re quests. And, fundamentally, all this shall be delivered at much higher levels of dependability, integrity and security. Increasingly, large parallel computing systems and networks are providing unique challenges to industry and academia in dependable computing, espe cially because of the higher failure rates intrinsic to these systems. The chal lenge in the last part of this decade is to build a systems that is both inexpensive and highly available. A machine cluster built of commodity hardware parts, with each node run ning an OS instance and a set of applications extended to be fault resilient can satisfy the new stringent high-availability requirements. The focus of this book is to present recent techniques and methods for im plementing fault-tolerant parallel and distributed computing systems. Section I, Fault-Tolerant Protocols, considers basic techniques for achieving fault-tolerance in communication protocols for distributed systems, including synchronous and asynchronous group communication, static total causal order ing protocols, and fail-aware datagram service that supports communications by time."

Multimedia Cartography provides a contemporary overview of the issues related to multimedia cartography and the design and production elements that are unique to this area of mapping. The book has been written for professional cartographers interested in moving into multimedia mapping, for cartographers already involved in producing multimedia titles who wish to discover the approaches that other practitioners in multimedia cartography have taken and for students and academics in the mapping sciences and related geographical fields wishing to update their knowledge about current issues related to cartographic design and production. It provides a new approach to cartography one based on the exploitation of the many rich media components and avant-garde approach that multimedia offers."

Handbook of Economic Expectations discusses the state-of-the-art in the collection, study and use of expectations data in economics, including the modelling of expectations formation and updating, as well as open questions and directions for future research. The book spans a broad range of fields, approaches and applications using data on subjective expectations that allows us to make progress on fundamental questions around the formation and updating of expectations by economic agents and their information sets. The information included will help us study heterogeneity and potential biases in expectations and analyze impacts on behavior and decision-making under uncertainty.

New state-of-the-art techniques for analyzing and managing Web data have emerged due to the need for dealing with huge amounts of data which are circulated on the Web. ""Web Data Management Practices: Emerging Techniques and Technologies"" provides a thorough understanding of major issues, current practices, and the main ideas in the field of Web data management, helping readers to identify current and emerging issues, as well as future trends in this area. ""Web Data Management Practices: Emerging Techniques and Technologies"" presents a complete overview of important aspects related to Web data management practices, such as: Web mining, Web data clustering, and others. This book also covers an extensive range of topics, including related issues about Web mining, Web caching and replication, Web services, and the XML standard.

This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data. Over 170 figures are included in the book.

Multiprocessing: Trade-Offs in Computation and Communication presents an in-depth analysis of several commonly observed regular and irregular computations for multiprocessor systems. This book includes techniques which enable researchers and application developers to quantitatively determine the effects of algorithm data dependencies on execution time, on communication requirements, on processor utilization and on the speedups possible. Starting with simple, two-dimensional, diamond-shaped directed acyclic graphs, the analysis is extended to more complex and higher dimensional directed acyclic graphs. The analysis allows for the quantification of the computation and communication costs and their interdependencies. The practical significance of these results on the performance of various data distribution schemes is clearly explained. Using these results, the performance of the parallel computations are formulated in an architecture independent fashion. These formulations allow for the parameterization of the architecture specitific entities such as the computation and communication rates. This type of parameterized performance analysis can be used at compile time or at run-time so as to achieve the most optimal distribution of the computations. The material in Multiprocessing: Trade-Offs in Computation and Communication connects theory with practice, so that the inherent performance limitations in many computations can be understood, and practical methods can be devised that would assist in the development of software for scalable high performance systems.