This book discusses the challenges in the convergence of technologies as the Internet of Things (IoT) evolves. These include sensing, computing, information processing, networking, and controlling intelligent technologies. The contributors first provide a survey of various assessment and evaluation approaches available for successful convergence. They then go on to cover several operational ideas to apply. The contributors then discuss the challenges involved bridging gaps in computation and the communication process, hidden networks, intelligent decision making, human-to-machine perception and large-scale IoT environments. The contributors aim to provide the reader an overview of trends in IoT in terms of performability and traffic modeling and efforts that can be spent in assessing the graceful degradation in IoT paradigms. Provides a survey of IoT assessment and evaluation approaches; Covers new and innovative operational ideas that apply to the IoT industry and the industries it affects; Includes chapters from researchers and industry leaders in IoT from around the world.

Innovations in Database Design, Web Applications, and Information Systems Management presents ideal research in the areas of database theory, systems design, ontologies, and many more. Including examples of the convergence of ideas from various disciplines aimed at improving and developing the theory of information technology and management of information resources, this book is useful for researchers and practitioners in the IT field.

This book investigates the coordinated power management of multi-tenant data centers that account for a large portion of the data center industry. The authors include discussion of their quick growth and their electricity consumption, which has huge economic and environmental impacts. This book covers the various coordinated management solutions in the existing literature focusing on efficiency, sustainability, and demand response aspects. First, the authors provide a background on the multi-tenant data center covering the stake holders, components, power infrastructure, and energy usage. Then, each power management mechanism is described in terms of motivation, problem formulation, challenges and solution.

This edited volume presents a collection of lessons learned with, and research conducted on, OpenStreetMap, the goal being to promote the project's integration. The respective chapters address a) state-of-the-art and cutting-edge approaches to data quality analysis in OpenStreetMap, b) investigations on understanding OpenStreetMap contributors and the nature of their contributions, c) identifying patterns of contributions and contributors, d) applications of OpenStreetMap in different domains, e) mining value-added knowledge and information from OpenStreetMap, f) limitations in the analysis OpenStreetMap data, and g) integrating OpenStreetMap with commercial and non-commercial datasets. The book offers an ideal opportunity to present and disseminate a number of cutting-edge developments and applications in the field of geography, spatial statistics, GIS, social science, and cartography.

"Managing Data in Motion" describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment.

The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired.

The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. "Managing Data in Motion" tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects.
Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data typesExplains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"

Commercial Web search engines such as Google, Yahoo, and Bing are used every day by millions of people across the globe. With their ever-growing refinement and usage, it has become increasingly difficult for academic researchers to keep up with the collection sizes and other critical research issues related to Web search, which has created a divide between the information retrieval research being done within academia and industry. Such large collections pose a new set of challenges for information retrieval researchers. In this work, Metzler describes highly effective information retrieval models for both smaller, classical data sets, and larger Web collections. In a shift away from heuristic, hand-tuned ranking functions and complex probabilistic models, he presents feature-based retrieval models. The Markov random field model he details goes beyond the traditional yet ill-suited bag of words assumption in two ways. First, the model can easily exploit various types of dependencies that exist between query terms, eliminating the term independence assumption that often accompanies bag of words models. Second, arbitrary textual or non-textual features can be used within the model. As he shows, combining term dependencies and arbitrary features results in a very robust, powerful retrieval model. In addition, he describes several extensions, such as an automatic feature selection algorithm and a query expansion framework. The resulting model and extensions provide a flexible framework for highly effective retrieval across a wide range of tasks and data sets. A Feature-Centric View of Information Retrieval provides graduate students, as well as academic and industrial researchers in the fields of information retrieval and Web search with a modern perspective on information retrieval modeling and Web searches.

COOP 2012 is the tenth COOP conference, marking twenty years from the first conference in 1992. In this special anniversary edition we asked researchers and practitioners to reflect on what have been the successes and the failures in designing cooperative systems, and what challenges still need to be addressed. We have come a long way in understanding the intricacies of cooperation and in designing systems that support work practices and collective activities. These advances would not have been possible without the concerted effort of contributions from a plethora of domains including CSCW, HCI, Information Systems, Knowledge Engineering, Multi-agent systems, organizational and management sciences, sociology, psychology, anthropology, ergonomics, linguistics, etc. The COOP community is going from strength to strength in developing new technologies, advancing and proposing new methodological approaches, and forging theories.

This book presents a unified framework, based on specialized evolutionary algorithms, for the global induction of various types of classification and regression trees from data. The resulting univariate or oblique trees are significantly smaller than those produced by standard top-down methods, an aspect that is critical for the interpretation of mined patterns by domain analysts. The approach presented here is extremely flexible and can easily be adapted to specific data mining applications, e.g. cost-sensitive model trees for financial data or multi-test trees for gene expression data. The global induction can be efficiently applied to large-scale data without the need for extraordinary resources. With a simple GPU-based acceleration, datasets composed of millions of instances can be mined in minutes. In the event that the size of the datasets makes the fastest memory computing impossible, the Spark-based implementation on computer clusters, which offers impressive fault tolerance and scalability potential, can be applied.

The Semantic Web is characterized by the existence of a very large number of distributed semantic resources, which together define a network of ontologies. These ontologies in turn are interlinked through a variety of different meta-relationships such as versioning, inclusion, and many more. This scenario is radically different from the relatively narrow contexts in which ontologies have been traditionally developed and applied, and thus calls for new methods and tools to effectively support the development of novel network-oriented semantic applications.

This book by Suarez-Figueroa et al. provides the necessary methodological and technological support for the development and use of ontology networks, which ontology developers need in this distributed environment. After an introduction, in its second part the authors describe the NeOn Methodology framework. The book's third part details the key activities relevant to the ontology engineering life cycle. For each activity, a general introduction, methodological guidelines, and practical examples are provided. The fourth part then presents a detailed overview of the NeOn Toolkit and its plug-ins. Lastly, case studies from the pharmaceutical and the fishery domain round out the work.

The book primarily addresses two main audiences: students (and their lecturers) who need a textbook for advanced undergraduate or graduate courses on ontology engineering, and practitioners who need to develop ontologies in particular or Semantic Web-based applications in general. Its educational value is maximized by its structured approach to explaining guidelines and combining them with case studies and numerous examples. The description of the open source NeOn Toolkit provides an additional asset, as it allows readers to easily evaluate and apply the ideas presented."

This book provides a critical examination of how the choice of what to believe is represented in the standard model of belief change. In particular the use of possible worlds and infinite remainders as objects of choice is critically examined. Descriptors are introduced as a versatile tool for expressing the success conditions of belief change, addressing both local and global descriptor revision. The book presents dynamic descriptors such as Ramsey descriptors that convey how an agent's beliefs tend to be changed in response to different inputs. It also explores sentential revision and demonstrates how local and global operations of revision by a sentence can be derived as a special case of descriptor revision. Lastly, the book examines revocation, a generalization of contraction in which a specified sentence is removed in a process that may possibly also involve the addition of some new information to the belief set.

This book highlights technical advances in knowledge management and their applications across a diverse range of domains. It explores the applications of knowledge computing methodologies in image processing, pattern recognition, health care and industrial contexts. The chapters also examine the knowledge engineering process involved in information management. Given its interdisciplinary nature, the book covers methods for identifying and acquiring valid, potentially useful knowledge sources. The ideas presented in the respective chapters illustrate how to effectively apply the perspectives of knowledge computing in specialized domains.

This in-depth book addresses a key void in the literature surrounding the Internet of Things (IoT) and health. By systematically evaluating the benefits of mobile, wireless, and sensor-based IoT technologies when used in health and wellness contexts, the book sheds light on the next frontier for healthcare delivery. These technologies generate data with significant potential to enable superior care delivery, self-empowerment, and wellness management. Collecting valuable insights and recommendations in one accessible volume, chapter authors identify key areas in health and wellness where IoT can be used, highlighting the benefits, barriers, and facilitators of these technologies as well as suggesting areas for improvement in current policy and regulations. Four overarching themes provide a suitable setting to examine the critical insights presented in the 31 chapters: Mobile- and sensor-based solutions Opportunities to incorporate critical aspects of analytics to provide superior insights and thus support better decision-making Critical issues around aspects of IoT in healthcare contexts Applications of portals in healthcare contexts A comprehensive overview that introduces the critical issues regarding the role of IoT technologies for health, Delivering Superior Health and Wellness Management with IoT and Analytics paves the way for scholars, practitioners, students, and other stakeholders to understand how to substantially improve health and wellness management on a global scale.

Knowledge Discovery and Data mining (KDD) is dedicated to exploring meaningful information from a large volume of data. ""Knowledge Discovery and Data Mining: Challenges and Realities"" is the most comprehensive reference publication for researchers and real-world data mining practitioners to advance knowledge discovery from low-quality data. This premier reference source presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying, low-quality data. International experts in the field of data mining have contributed all-inclusive chapters focusing on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing.

Information and communication technologies of the 20th century have had a significant impact on our daily lives. They have brought new opportunities as well as new challenges for human development. The Philosopher: Luciano Floridi claims that these new technologies have led to a revolutionary shift in our understanding of humanity's nature and its role in the universe. Florodi's philosophical analysis of new technologies leads to a novel metaphysical framework in which our understanding of the ultimate nature of reality shifts from a materialist one to an informational one. In this world, all entities, be they natural or artificial, are analyzed as informational entities. This book provides critical reflection to this idea, in four different areas: Information Ethics and The Method of Levels of Abstraction The Information Revolution and Alternative Categorizations of Technological Advancements Applications: Education, Internet and Information Science Epistemic and Ontic Aspects of the Philosophy of Information

This book focuses on new methods, architectures, and applications for the management of Cyber Physical Objects (CPOs) in the context of the Internet of Things (IoT). It covers a wide range of topics related to CPOs, such as resource management, hardware platforms, communication and control, and control and estimation over networks. It also discusses decentralized, distributed, and cooperative optimization as well as effective discovery, management, and querying of CPOs. Other chapters outline the applications of control, real-time aspects, and software for CPOs and introduce readers to agent-oriented CPOs, communication support for CPOs, real-world deployment of CPOs, and CPOs in Complex Systems. There is a focus on the importance of application of IoT technologies for Smart Cities.

In many decision support fields, the data that is exploited is becoming more and more complex. To take this phenomenon into account, classical architectures of data warehouses or data mining algorithms must be completely re-evaluated. ""Processing and Managing Complex Data for Decision Support"" provides readers with an overview of the emerging field of complex data processing by bringing together various research studies and surveys in different subfields, and by highlighting the similarities between the different data, issues, and approaches. This book deals with important topics, such as: complex data warehousing, including spatial, XML, and text warehousing; and complex data mining, including distance metrics and similarity measures, pattern management, multimedia, and gene sequence mining.

This book brings together scientists, researchers, practitioners, and students from academia and industry to present recent and ongoing research activities concerning the latest advances, techniques, and applications of natural language processing systems, and to promote the exchange of new ideas and lessons learned. Taken together, the chapters of this book provide a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of intelligent natural language processing. The book presents the state-of-the-art in research on natural language processing, computational linguistics, applied Arabic linguistics and related areas. New trends in natural language processing systems are rapidly emerging - and finding application in various domains including education, travel and tourism, and healthcare, among others. Many issues encountered during the development of these applications can be resolved by incorporating language technology solutions. The topics covered by the book include: Character and Speech Recognition; Morphological, Syntactic, and Semantic Processing; Information Extraction; Information Retrieval and Question Answering; Text Classification and Text Mining; Text Summarization; Sentiment Analysis; Machine Translation Building and Evaluating Linguistic Resources; and Intelligent Language Tutoring Systems.

This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

This book presents a mathematical treatment of the radio resource allocation of modern cellular communications systems in contested environments. It focuses on fulfilling the quality of service requirements of the living applications on the user devices, which leverage the cellular system, and with attention to elevating the users' quality of experience. The authors also address the congestion of the spectrum by allowing sharing with the band incumbents while providing with a quality-of-service-minded resource allocation in the network. The content is of particular interest to telecommunications scheduler experts in industry, communications applications academia, and graduate students whose paramount research deals with resource allocation and quality of service.

The "EPCglobal Architecture Framework" is currently the most accepted technical approach to the Internet of Things and provides a solid foundation for building Business-to-Business information networks based on unique identifications of 'things'. Lately, the vision of the Internet of Things has been extended to a more holistic approach that integrates sensors as well as actuators and includes non-business stakeholders. A detailed look at the current state of the art in
research concerning cost and benefit estimations is provided and the limits of Cost Benefit Sharing for RFID-based IT-infrastructures are explained. In this work a market driven evaluation based on sales potential of information is developed. Simple technical means for aggregation of micro values to a billable amount are discussed. The requirements for electronic billing infrastructure are defined and a matching e-billing solutions for an evaluation scenario is presented, providing a technical infrastructure to evaluate and bill product-related information in a future Internet of Things, based on an extended EPCglobal Architecture. Further opportunities and threats are discussed to provide an overview of its future potential. As a result it will contribute to the sustainable success of the Internet of Things itself.

This book constitutes the refereed proceedings of the 4th IFIP WG 8.1 Working Conference on Method Engineering, ME 2011, held in Paris, France, in April 2011. The 13 revised full papers and 6 short papers presented together with the abstracts of two keynote talks were carefully reviewed and selected from 30 submissions. The papers are organized in topical sections on situated method engineering, method engineering foundations, customized methods, tools for method engineering, new trends to build methods, and method engineering services.

In this book, the editors explain how students enrolled in two digital forensic courses at their institution are exposed to experiential learning opportunities, where the students acquire the knowledge and skills of the subject-matter while also learning how to adapt to the ever-changing digital forensic landscape. Their findings (e.g., forensic examination of different IoT devices) are also presented in the book. Digital forensics is a topic of increasing importance as our society becomes "smarter" with more of the "things" around us been internet- and inter-connected (e.g., Internet of Things (IoT) and smart home devices); thus, the increasing likelihood that we will need to acquire data from these things in a forensically sound manner. This book is of interest to both digital forensic educators and digital forensic practitioners, as well as students seeking to learn about digital forensics.

This book sheds new light on the current and future challenges faced by cities, and presents approaches, options and solutions enabled by Information and Communication Technologies (ICT) in the smart city context. By focusing on sustainability objectives within a rapidly changing social, economic, environmental and technological setting, it explores a variety of planning challenges faced by contemporary cities and the power of smart city developments in terms of providing innovative tools, approaches, methodologies and technologies to help cities cope with these challenges. Key issues addressed include smart city (e-) planning and (e-)participation; smart data management to facilitate decision-making processes in cities and insular communities on a variety of topics; smart and sustainable management aspects of climate change, water scarcity, mobility, energy, infrastructure, tourism, blue growth, risk assessment; etc. The book presents current and potential pathways and applications for the evolution of smart cities and communities, taking into consideration the unique problems and opportunities emanating from their specific geographical location. The case study examples mainly concern small and medium-sized cities and communities as well as insular areas in the Mediterranean region, while also incorporating lessons learned from other parts of the world. Their focus is on the specific opportunities and threats emerging in these urban and insular environments, which are characterized by their role as globally known tourist destinations, their coastal or port character, and unique cultural resources, as well as the high rated vulnerability in very many sustainability respects (social, economic, biodiversity, urbanization, migration, poverty, etc.) to be found in the Mediterranean region at large