In the mid 1990s, Tim Berners-Lee had the idea of developing the World Wide Web into a "Semantic Web", a web of information that could be interpreted by machines in order to allow the automatic exploitation of data, which until then had to be done by humans manually. One of the first people to research topics related to the Semantic Web was Professor Rudi Studer. From the beginning, Rudi drove projects like ONTOBROKER and On-to-Knowledge, which later resulted in W3C standards such as RDF and OWL. By the late 1990s, Rudi had established a research group at the University of Karlsruhe, which later became the nucleus and breeding ground for Semantic Web research, and many of today's well-known research groups were either founded by his disciples or benefited from close cooperation with this think tank. In this book, published in celebration of Rudi's 60th birthday, many of his colleagues look back on the main research results achieved during the last 20 years. Under the editorship of Dieter Fensel, once one of Rudi's early PhD students, an impressive list of contributors and contributions has been collected, covering areas like Knowledge Management, Ontology Engineering, Service Management, and Semantic Search. Overall, this book provides an excellent overview of the state of the art in Semantic Web research, by combining historical roots with the latest results, which may finally make the dream of a "Web of knowledge, software and services" come true.

This book covers the latest advances in Big Data technologies and provides the readers with a comprehensive review of the state-of-the-art in Big Data processing, analysis, analytics, and other related topics. It presents new models, algorithms, software solutions and methodologies, covering the full data cycle, from data gathering to their visualization and interaction, and includes a set of case studies and best practices. New research issues, challenges and opportunities shaping the future agenda in the field of Big Data are also identified and presented throughout the book, which is intended for researchers, scholars, advanced students, software developers and practitioners working at the forefront in their field.

As economies continue to evolve, knowledge is being recognized as a business asset and considered a crucial component of business strategy. The ability to manage knowledge is increasingly important for securing and maintaining organizational success and surviving in the knowledge economy. ""Knowledge Management Strategies for Business Development"" addresses the relevance of knowledge management strategies for the advancement of organizations worldwide. This reference book supplies business practitioners, academicians, and researchers with comprehensive tools to systematically guide through a process that focuses on data gathering, analysis, and decision making.

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.

This monograph will provide an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate (FDR) and related error measures, particularly addressing applications to fields such as genetics, proteomics, neuroscience and general biology. The book will also include a detailed description how to implement these methods in practice. Moreover new developments focusing on non-standard assumptions are also included, especially multiple tests for discrete data. The book primarily addresses researchers and practitioners but will also be beneficial for graduate students.

This book presents the latest research advances in complex network structure analytics based on computational intelligence (CI) approaches, particularly evolutionary optimization. Most if not all network issues are actually optimization problems, which are mostly NP-hard and challenge conventional optimization techniques. To effectively and efficiently solve these hard optimization problems, CI based network structure analytics offer significant advantages over conventional network analytics techniques. Meanwhile, using CI techniques may facilitate smart decision making by providing multiple options to choose from, while conventional methods can only offer a decision maker a single suggestion. In addition, CI based network structure analytics can greatly facilitate network modeling and analysis. And employing CI techniques to resolve network issues is likely to inspire other fields of study such as recommender systems, system biology, etc., which will in turn expand CI's scope and applications. As a comprehensive text, the book covers a range of key topics, including network community discovery, evolutionary optimization, network structure balance analytics, network robustness analytics, community-based personalized recommendation, influence maximization, and biological network alignment. Offering a rich blend of theory and practice, the book is suitable for students, researchers and practitioners interested in network analytics and computational intelligence, both as a textbook and as a reference work.

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.

Imagine yourself as a military officer in a conflict zone trying to identify locations of weapons caches supporting road-side bomb attacks on your country's troops. Or imagine yourself as a public health expert trying to identify the location of contaminated water that is causing diarrheal diseases in a local population. Geospatial abduction is a new technique introduced by the authors that allows such problems to be solved. Geospatial Abduction provides the mathematics underlying geospatial abduction and the algorithms to solve them in practice; it has wide applicability and can be used by practitioners and researchers in many different fields. Real-world applications of geospatial abduction to military problems are included. Compelling examples drawn from other domains as diverse as criminology, epidemiology and archaeology are covered as well. This book also includes access to a dedicated website on geospatial abduction hosted by University of Maryland. Geospatial Abduction targets practitioners working in general AI, game theory, linear programming, data mining, machine learning, and more. Those working in the fields of computer science, mathematics, geoinformation, geological and biological science will also find this book valuable.

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.

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.

This book presents the latest on the theoretical approach of the contemporary issues evolved in strategic marketing and the integration of theory and practice. It seeks to make advancements in the discipline by promoting strategic research and innovative activities in marketing. The book highlights the use of data analytics, intelligence and knowledge-based systems in this area. In the era of knowledge-based economy, marketing has a lot to gain from collecting and analyzing data associated with customers, business processes, market economics or even data related to social activities. The contributed chapters are concerned with using modern qualitative and quantitative techniques based on information technology used to manage and analyze business data, to discover hidden knowledge and to introduce intelligence into marketing processes. This allows for a focus on innovative applications in all aspects of marketing, of computerized technologies related to data analytics, predictive analytics and modeling, business intelligence and knowledge engineering, in order to demonstrate new ways of uncovering hidden knowledge and supporting marketing decisions with evidence-based intelligent tools. Among the topics covered include innovative tourism marketing strategies, marketing communications in small and medium-sized enterprises (SMEs), the use of business modeling, as well as reflecting on the marketing trends and outlook for all transportation industry segments. The papers in this proceedings has been written by scientists, researchers, practitioners and students that demonstrate a special orientation in strategic marketing, all of whom aspire to be ahead of the curve based on the pillars of innovation. This proceedings volume compiles their contributions to the field, highlighting the exchange of insights on strategic issues in the science of innovation marketing.

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.

Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this book. As the authors show, modern visual analytics techniques are ready to tackle the enormous challenges brought about by movement data, and the technology and software needed to exploit them are available today. The authors start by illustrating the different kinds of data available to describe movement, from individual trajectories of single objects to multiple trajectories of many objects, and then proceed to detail a conceptual framework, which provides the basis for a fundamental understanding of movement data. With this basis, they move on to more practical and technical aspects, focusing on how to transform movement data to make it more useful, and on the infrastructure necessary for performing visual analytics in practice. In so doing they demonstrate that visual analytics of movement data can yield exciting insights into the behavior of moving persons and objects, but can also lead to an understanding of the events that transpire when things move. Throughout the book, they use sample applications from various domains and illustrate the examples with graphical depictions of both the interactive displays and the analysis results. In summary, readers will benefit from this detailed description of the state of the art in visual analytics in various ways. Researchers will appreciate the scientific precision involved, software technologists will find essential information on algorithms and systems, and practitioners will profit from readily accessible examples with detailed illustrations for practical purposes.

Following the introduction of Myspace, Linkedin and Facebook, social networks have become part of people's everyday life. New online social networks appear almost daily developed to attract the attention of Internet users. However, design and implementation standards and guidelines are often sacrificed for innovation resulting in unusable social networking platforms destined to be doomed. Within this context, the present book recommends a design and implementation methodology which will allow future social networking platform designers and developers to work in a scientifically systematic and sound manner to reach their goal. The journey begins with the identification of the pursued objective of the social network and the potential participants. The presentation of various methods and techniques to collect the requirements of the intended user group of the social networking platform follows. Best practices, guidelines and standards that will facilitate the conceptual and physical design of the platform are portrayed. An analysis of advantages and limitations for existing implementation platforms and potential implementation techniques is subsequently offered to facilitate the selection of the most appropriate tool for the implementation of the online social networking website. Various representative visualization techniques are examined in order to enhance the visual representation of the social network participants. Various usability evaluation techniques are then presented to aid in the assessment of usability and improvement of the actual user experience. Finally, future trends in the design and use of social networks are presented.

This book features selected papers presented at the International Conference on Information Management and Machine Intelligence (ICIMMI 2019), held at the Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India, on December 14-15, 2019. It covers a range of topics, including data analytics; AI; machine and deep learning; information management, security, processing techniques and interpretation; applications of artificial intelligence in soft computing and pattern recognition; cloud-based applications for machine learning; application of IoT in power distribution systems; as well as wireless sensor networks and adaptive wireless communication.

Data processing has become essential to modern civilization. The original data for this processing comes from measurements or from experts, and both sources are subject to uncertainty. Traditionally, probabilistic methods have been used to process uncertainty. However, in many practical situations, we do not know the corresponding probabilities: in measurements, we often only know the upper bound on the measurement errors; this is known as interval uncertainty. In turn, expert estimates often include imprecise (fuzzy) words from natural language such as "small"; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination describe state-of-the-art developments in their research areas. Accordingly, the book offers a valuable guide for researchers and practitioners interested in data processing under uncertainty, and an introduction to the latest trends and techniques in this area, suitable for graduate students.

This open access book is a step-by-step introduction on how shell scripting can help solve many of the data processing tasks that Health and Life specialists face everyday with minimal software dependencies. The examples presented in the book show how simple command line tools can be used and combined to retrieve data and text from web resources, to filter and mine literature, and to explore the semantics encoded in biomedical ontologies. To store data this book relies on open standard text file formats, such as TSV, CSV, XML, and OWL, that can be open by any text editor or spreadsheet application. The first two chapters, Introduction and Resources, provide a brief introduction to the shell scripting and describe popular data resources in Health and Life Sciences. The third chapter, Data Retrieval, starts by introducing a common data processing task that involves multiple data resources. Then, this chapter explains how to automate each step of that task by introducing the required commands line tools one by one. The fourth chapter, Text Processing, shows how to filter and analyze text by using simple string matching techniques and regular expressions. The last chapter, Semantic Processing, shows how XPath queries and shell scripting is able to process complex data, such as the graphs used to specify ontologies. Besides being almost immutable for more than four decades and being available in most of our personal computers, shell scripting is relatively easy to learn by Health and Life specialists as a sequence of independent commands. Comprehending them is like conducting a new laboratory protocol by testing and understanding its procedural steps and variables, and combining their intermediate results. Thus, this book is particularly relevant to Health and Life specialists or students that want to easily learn how to process data and text, and which in return may facilitate and inspire them to acquire deeper bioinformatics skills in the future.

Recently, researchers have gained innovative principles, methods, algorithms, and solutions to challenging problems faced in the development of data warehousing, knowledge discovery, and data mining applications. Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications provides a comprehensive analysis on current issues and trends in retrieval expansion. Containing research from leading international experts, this book presents future challenges and opportunities in the field valuable to academicians, researchers, and practitioners.

This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.

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 presents best selected papers presented at the International Conference on Paradigms of Computing, Communication and Data Sciences (PCCDS 2020), organized by National Institute of Technology, Kurukshetra, India, during 1-3 May 2020. It discusses high-quality and cutting-edge research in the areas of advanced computing, communications and data science techniques. The book is a collection of latest research articles in computation algorithm, communication and data sciences, intertwined with each other for efficiency.

This book examines the managerial dimensions of business intelligence (BI) systems. It develops a set of guidelines for value creation by implementing business intelligence systems and technologies. In particular the book looks at BI as a process - driven by a mix of human and technological capabilities - to serve complex information needs in building insights and providing aid in decision making. After an introduction to the key concepts of BI and neighboring areas of information processing, the book looks at the complexity and multidimensionality of BI. It tackles both data integration and information integration issues. Bodies of knowledge and other widely accepted collections of experience are presented and turned into lessons learned. Following a straightforward introduction to the processes and technologies of BI the book embarks on BI maturity and agility, the components, drivers and inhibitors of BI culture and soft BI factors like attention, sense and trust. Eventually the book attempts to provide a holistic view on business intelligence, possible structures and tradeoffs and embarks to provide an outlook on possible developments in BI and analytics.

This book provides extensive insight into the possibilities and challenges of XML in building new information management solutions in networked organizations. After a brief introduction to Web communication features and XML fundamentals, the book examines the benefits of adopting XML and illustrates various types of XML use: XML in document management; XML for data-centric and multimedia components; XML as a format for metadata, including metadata for the Semantic Web; and XML in support of data interchange between software applications and among organizations. The challenges of adopting XML in large-scale information management are also discussed. In addition, applications across a broad spectrum are examined and numerous case studies pertaining to the adoption of XML are presented. The book is particularly suitable for courses offered in Information Studies, Information Systems, or Information Technology. It also serves as an excellent practical guide for professionals in information management and provides important support material for courses in Computer Science and in Business.