This book highlights research that contributes to a better understanding of emerging challenges in information systems (IS) outsourcing. Important topics covered include: how to digitally innovate through IS outsourcing; how to govern outsourced digitalization projects; how to cope with complex multi-vendor and micro-services arrangements; how to manage data sourcing and data partnerships, including issues of cybersecurity; and how to cope with the increasing demands of internationalization and new sourcing models, such as crowdsourcing, cloud sourcing and robotic process automation. These issues are approached from the client's perspective, vendor's perspective, or both. Given its scope, the book will be of interest to all researchers and students in the fields of Information Systems, Management, and Organization, as well as corporate executives and professionals seeking a more profound analysis of the underlying factors and mechanisms of outsourcing.

Foundations of Dependable Computing: System Implementation, explores the system infrastructure needed to support the various paradigms of Paradigms for Dependable Applications. Approaches to implementing support mechanisms and to incorporating additional appropriate levels of fault detection and fault tolerance at the processor, network, and operating system level are presented. A primary concern at these levels is balancing cost and performance against coverage and overall dependability. As these chapters demonstrate, low overhead, practical solutions are attainable and not necessarily incompatible with performance considerations. The section on innovative compiler support, in particular, demonstrates how the benefits of application specificity may be obtained while reducing hardware cost and run-time overhead. A companion to this volume (published by Kluwer) subtitled Models and Frameworks for Dependable Systems presents two comprehensive frameworks for reasoning about system dependability, thereby establishing a context for understanding the roles played by specific approaches presented in this book's two companion volumes. It then explores the range of models and analysis methods necessary to design, validate and analyze dependable systems. Another companion to this book (published by Kluwer), subtitled Paradigms for Dependable Applications, presents a variety of specific approaches to achieving dependability at the application level. Driven by the higher level fault models of Models and Frameworks for Dependable Systems, and built on the lower level abstractions implemented in a third companion book subtitled System Implementation, these approaches demonstrate how dependability may be tuned to the requirements of an application, the fault environment, and the characteristics of the target platform. Three classes of paradigms are considered: protocol-based paradigms for distributed applications, algorithm-based paradigms for parallel applications, and approaches to exploiting application semantics in embedded real-time control systems.

Recently, IT has entered all important areas of society. Enterprises, individuals and civilisations all depend on functioning, safe and secure IT. Focus on IT security has previously been fractionalised, detailed and often linked to non-business applicaitons. The aim of this book is to address the current and future prospects of modern IT security, functionality in business, trade, industry, health care and government. The main topic areas covered include existing IT security tools and methodology for modern IT environments, laws, regulations and ethics in IT security environments, current and future prospects in technology, infrastructures, technique and methodology and IT security in retrospective.

The primary aim for this book is to gather and collate articles which represent the best and latest thinking in the domain of technology transfer, from research, academia and practice around the world. We envisage that the book will, as a result of this, represent an important source of knowledge in this domain to students (undergraduate and postgraduate), researchers, practitioners and consultants, chiefly in the software engineering and IT/industries, but also in management and other organisational and social disciplines. An important aspect of the book is the role that reflective practitioners (and not just academics) play. They will be involved in the production, and evaluation of contributions, as well as in the design and delivery of conference events, upon which of course, the book will be based.

Text Retrieval and Filtering: Analytical Models of Performance is the first book that addresses the problem of analytically computing the performance of retrieval and filtering systems. The book describes means by which retrieval may be studied analytically, allowing one to describe current performance, predict future performance, and to understand why systems perform as they do. The focus is on retrieving and filtering natural language text, with material addressing retrieval performance for the simple case of queries with a single term, the more complex case with multiple terms, both with term independence and term dependence, and for the use of grammatical information to improve performance. Unambiguous statements of the conditions under which one method or system will be more effective than another are developed. Text Retrieval and Filtering: Analytical Models of Performance focuses on the performance of systems that retrieve natural language text, considering full sentences as well as phrases and individual words. The last chapter explicitly addresses how grammatical constructs and methods may be studied in the context of retrieval or filtering system performance. The book builds toward solving this problem, although the material in earlier chapters is as useful to those addressing non-linguistic, statistical concerns as it is to linguists. Those interested in grammatical information should be cautioned to carefully examine earlier chapters, especially Chapters 7 and 8, which discuss purely statistical relationships between terms, before moving on to Chapter 10, which explicitly addresses linguistic issues. Text Retrieval and Filtering: Analytical Models of Performance is suitable as a secondary text for a graduate level course on Information Retrieval or Linguistics, and as a reference for researchers and practitioners in industry.

This book discusses the advancements in artificial intelligent techniques used in the well-being of human healthcare. It details the techniques used in collection, storage and analysis of data and their usage in different healthcare solutions. It also discusses the techniques of predictive analysis in early diagnosis of critical diseases. The edited book is divided into four parts - part A discusses introduction to artificial intelligence and machine learning in healthcare; part B highlights different analytical techniques used in healthcare; part C provides various security and privacy mechanisms used in healthcare; and finally, part D exemplifies different tools used in visualization and data analytics.

This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.

Wikipedia, Flickr, You Tube, Facebook, LinkedIn are all examples of large community-built databases, although with quite diverse purposes and collaboration patterns. Their usage and dissemination will further grow introducing e.g. new semantics, personalization, or interactive media.

Pardede delivers the first comprehensive research reference on community-built databases. The contributions discuss various technical and social aspects of research in and development in areas like in Web science, social networks, and collaborative information systems.

Pardede delivers the first comprehensive research reference on community-built databases. The contributions discuss various technical and social aspects of research in and development in areas like in Web science, social networks, and collaborative information systems.

This book explores the core themes of the Fourth Industrial Revolution (4IR) highlighting the digital transformation that has been occurring in society and business. Representing an interface between technologies in the physical, digital and biological disciplines the book explores emerging technologies such as artificial intelligence, robotics, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing. The findings of collaborative research studies on the potential impact of the 4IR on the labour markets, occupations, future workforce competencies and skills associated with eight industry sectors in Australia are reported. The sectors are: agriculture and mining; manufacturing and logistics; health, medical and nursing; education; retail; financial services; government services and tourism.

This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

Foundations of Dependable Computing: Paradigms for Dependable Applications, presents a variety of specific approaches to achieving dependability at the application level. Driven by the higher level fault models of Models and Frameworks for Dependable Systems, and built on the lower level abstractions implemented in a third companion book subtitled System Implementation, these approaches demonstrate how dependability may be tuned to the requirements of an application, the fault environment, and the characteristics of the target platform. Three classes of paradigms are considered: protocol-based paradigms for distributed applications, algorithm-based paradigms for parallel applications, and approaches to exploiting application semantics in embedded real-time control systems. The companion volume subtitled Models and Frameworks for Dependable Systems presents two comprehensive frameworks for reasoning about system dependability, thereby establishing a context for understanding the roles played by specific approaches presented in this book's two companion volumes. It then explores the range of models and analysis methods necessary to design, validate and analyze dependable systems. Another companion book (published by Kluwer) subtitled System Implementation, explores the system infrastructure needed to support the various paradigms of Paradigms for Dependable Applications. Approaches to implementing support mechanisms and to incorporating additional appropriate levels of fault detection and fault tolerance at the processor, network, and operating system level are presented. A primary concern at these levels is balancing cost and performance against coverage and overall dependability. As these chapters demonstrate, low overhead, practical solutions are attainable and not necessarily incompatible with performance considerations. The section on innovative compiler support, in particular, demonstrates how the benefits of application specificity may be obtained while reducing hardware cost and run-time overhead.

Successfully competing in the new global economy requires immediate decision capability. This immediate decision capability requires quick analysis of both timely and relevant data. To support this analysis, organizations are piling up mountains of business data in their databases every day. Terabyte-sized (1,000 megabytes) databases are commonplace in organizations today, and this enormous growth will make petabyte-sized databases (1,000 terabytes) a reality within the next few years (Whiting, 2002). Those organizations making swift, fact-based decisions by optimally leveraging their data resources will outperform those organizations that do not. A technology that facilitates this process of optimal decision-making is known as Organizational Data Mining (ODM). Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance demonstrates how organizations can leverage ODM for enhanced competitiveness and optimal performance.

Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., "most students used to be profitable") and the patterns of the future (e.g., "students will be profitable").

Information retrieval is the science concerned with the effective and efficient retrieval of documents starting from their semantic content. It is employed to fulfill some information need from a large number of digital documents. Given the ever-growing amount of documents available and the heterogeneous data structures used for storage, information retrieval has recently faced and tackled novel applications.

In this book, Melucci and Baeza-Yates present a wide-spectrum illustration of recent research results in advanced areas related to information retrieval. Readers will find chapters on e.g. aggregated search, digital advertising, digital libraries, discovery of spam and opinions, information retrieval in context, multimedia resource discovery, quantum mechanics applied to information retrieval, scalability challenges in web search engines, and interactive information retrieval evaluation. All chapters are written by well-known researchers, are completely self-contained and comprehensive, and are complemented by an integrated bibliography and subject index.

With this selection, the editors provide the most up-to-date survey of topics usually not addressed in depth in traditional (text)books on information retrieval. The presentation is intended for a wide audience of people interested in information retrieval: undergraduate and graduate students, post-doctoral researchers, lecturers, and industrial researchers.

Foundations of Dependable Computing: Models and Frameworks for Dependable Systems presents two comprehensive frameworks for reasoning about system dependability, thereby establishing a context for understanding the roles played by specific approaches presented in this book's two companion volumes. It then explores the range of models and analysis methods necessary to design, validate and analyze dependable systems. A companion to this book (published by Kluwer), subtitled Paradigms for Dependable Applications, presents a variety of specific approaches to achieving dependability at the application level. Driven by the higher level fault models of Models and Frameworks for Dependable Systems, and built on the lower level abstractions implemented in a third companion book subtitled System Implementation, these approaches demonstrate how dependability may be tuned to the requirements of an application, the fault environment, and the characteristics of the target platform. Three classes of paradigms are considered: protocol-based paradigms for distributed applications, algorithm-based paradigms for parallel applications, and approaches to exploiting application semantics in embedded real-time control systems. Another companion book (published by Kluwer) subtitled System Implementation, explores the system infrastructure needed to support the various paradigms of Paradigms for Dependable Applications. Approaches to implementing support mechanisms and to incorporating additional appropriate levels of fault detection and fault tolerance at the processor, network, and operating system level are presented. A primary concern at these levels is balancing cost and performance against coverage and overall dependability. As these chapters demonstrate, low overhead, practical solutions are attainable and not necessarily incompatible with performance considerations. The section on innovative compiler support, in particular, demonstrates how the benefits of application specificity may be obtained while reducing hardware cost and run-time overhead.

Compression and Coding Algorithms describes in detail the coding mechanisms that are available for use in data compression systems. The well known Huffman coding technique is one mechanism, but there have been many others developed over the past few decades, and this book describes, explains and assesses them. People undertaking research of software development in the areas of compression and coding algorithms will find this book an indispensable reference. In particular, the careful and detailed description of algorithms and their implementation, plus accompanying pseudo-code that can be readily implemented on computer, make this book a definitive reference in an area currently without one.

In multimedia and communication environments all documents must be protected against attacks. The movie Forrest Gump showed how multimedia documents can be manipulated. The required security can be achieved by a number of different security measures. This book provides an overview of the current research in Multimedia and Communication Security. A broad variety of subjects are addressed including: network security; attacks; cryptographic techniques; healthcare and telemedicine; security infrastructures; payment systems; access control; models and policies; auditing and firewalls. This volume contains the selected proceedings of the joint conference on Communications and Multimedia Security; organized by the International Federation for Information processing and supported by the Austrian Computer Society, Gesellschaft fuer Informatik e.V. and TeleTrust Deutschland e.V. The conference took place in Essen, Germany, in September 1996

Sensor network data management poses new challenges outside the scope of conventional systems where data is represented and regulated. Intelligent Techniques for Warehousing and Mining Sensor Network Data presents fundamental and theoretical issues pertaining to data management. Covering a broad range of topics on warehousing and mining sensor networks, this advanced title provides significant industry solutions to those in database, data warehousing, and data mining research communities.

The Testability of Distributed Real-Time Systems starts by collecting and analyzing all principal problems, as well as their interrelations that one has to keep in mind wh4en testing a distributed real-time system. The book discusses them in some detail from the viewpoints of software engineering, distributed systems principles, and real-time system development. These problems are organization, observability, reproducibility, the host/target approach, environment simulation, and (test) representativity. Based on this framework, the book summarizes and evaluates the current work done in this area before going on to argue that the particular system architecture (hardware plus operating system) has a much greater influence on testing than is the case for ordinary', non-real-time software. The notions of event-triggered and time-triggered system architectures are introduced, and its is shown that time-triggered systems automatically' (i.e. by the nature of their system architecture) solve or greatly ease solving of some of the problems introduced earlier, i.e. observability, reproducibility, and (partly) representativity.A test methodology is derived for the time-triggered, distributed real-time system MARS. The book describes in detail how the author has taken advantage of its architecture, and shows how the remaining problems can be solved for this particular system architecture. Some experiments conducted to evaluate this test methodology are reported, including the experience gained from them, leading to a description of a number of prototype support tools.The Testability of Distributed Real-Time Systems can be used by both academic and industrial researchers interested in distributedand/or real-time systems, or in software engineering for such systems. This book can also be used as a text in advanced courses on distributed or real-time systems.

Information Macrodynamics (IMD) belong to an interdisciplinary science that represents a new theoretical and computer-based methodology for a system informational descriptionand improvement, including various activities in such areas as thinking, intelligent processes, communications, management, and other nonphysical subjects with their mutual interactions, informational superimposition, and theinformation transferredbetweeninteractions. The IMD is based on the implementation of a single concept by a unique mathematical principle and formalism, rather than on an artificial combination of many arbitrary, auxiliary concepts and/or postulates and different mathematical subjects, such as the game, automata, catastrophe, logical operations theories, etc. This concept is explored mathematically using classical mathematics as calculus of variation and the probability theory, which are potent enough, without needing to developnew, specifiedmathematical systemicmethods. The formal IMD model automatically includes the related results from other fields, such as linear, nonlinear, collective and chaotic dynamics, stability theory, theory of information, physical analogies of classical and quantum mechanics, irreversible thermodynamics, andkinetics. The main IMD goal is to reveal the information regularities, mathematically expressed by the considered variation principle (VP), as a mathematical tool to extractthe regularities and define the model, whichdescribes theregularities. The IMD regularities and mechanisms are the results of the analytical solutions and are not retained by logical argumentation, rational introduction, and a reasonable discussion. The IMD's information computer modeling formalism includes a human being (as an observer, carrier and producer ofinformation), with a restoration of the model during the objectobservations.

Advances In Digital Government presents a collection of in-depth articles that addresses a representative cross-section of the matrix of issues involved in implementing digital government systems. These articles constitute a survey of both the technical and policy dimensions related to the design, planning and deployment of digital government systems. The research and development projects within the technical dimension represent a wide range of governmental functions, including the provisioning of health and human services, management of energy information, multi-agency integration, and criminal justice applications. The technical issues dealt with in these projects include database and ontology integration, distributed architectures, scalability, and security and privacy. The human factors research emphasizes compliance with access standards for the disabled and the policy articles contain both conceptual models for developing digital government systems as well as real management experiences and results in deploying them. Advances In Digital Government presents digital government issues from the perspectives of different communities and societies. This geographic and social diversity illuminates a unique array of policy and social perspectives, exposing practitioners to new and useful ways of thinking about digital government.

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

In today's market, emerging technologies are continually assisting in common workplace practices as companies and organizations search for innovative ways to solve modern issues that arise. Prevalent applications including internet of things, big data, and cloud computing all have noteworthy benefits, but issues remain when separately integrating them into the professional practices. Significant research is needed on converging these systems and leveraging each of their advantages in order to find solutions to real-time problems that still exist. Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing is a pivotal reference source that provides vital research on the relation between these technologies and the impact they collectively have in solving real-world challenges. While highlighting topics such as cloud-based analytics, intelligent algorithms, and information security, this publication explores current issues that remain when attempting to implement these systems as well as the specific applications IoT, big data, and cloud computing have in various professional sectors. This book is ideally designed for academicians, researchers, developers, computer scientists, IT professionals, practitioners, scholars, students, and engineers seeking research on the integration of emerging technologies to solve modern societal issues.

Earth Observation interacts with space, remote sensing, communication, and information technologies, and plays an increasingly significant role in Earth related scientific studies, resource management, homeland security, topographic mapping, and development of a healthy, sustainable environment and community. Geospatial Technology for Earth Observation provides an in-depth and broad collection of recent progress in Earth observation. Contributed by leading experts in this field, the book covers satellite, airborne and ground remote sensing systems and system integration, sensor orientation, remote sensing physics, image classification and analysis, information extraction, geospatial service, and various application topics, including cadastral mapping, land use change evaluation, water environment monitoring, flood mapping, and decision making support. Geospatial Technology for Earth Observation serves as a valuable training source for researchers, developers, and practitioners in geospatial science and technology industry. It is also suitable as a reference book for upper level college students and graduate students in geospatial technology, geosciences, resource management, and informatics.

It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed "ensemble learning" by researchers in computational intelligence and machine learning, it is known to improve a decision system's robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as "boosting" and "random forest" facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics.

Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

"