Bøger i Advanced Information and Knowledge Processing serien

During the 21st century business environments have become more complex and dynamic than ever before. Companies operate in a world of change influenced by globalisation, volatile markets, legal changes and technical progress. As a result, they have to handle growing volumes of data and therefore require fast storage, reliable data access, intelligent retrieval of information and automated decision-making mechanisms, all provided at the highest level of service quality. Successful enterprises are aware of these challenges and efficiently respond to the dynamic environment in which their business operates.Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges and provide techniques to enable effective business change. The important aspects of both topics are discussed within this state-of-the-art volume. It covers the strategic support, business applications, methodologies and technologies from the field, and explores the benefits, issues and challenges of each. Issues are analysed from many different perspectives, ranging from strategic management to data technologies, and the different subjects are complimented and illustrated by numerous examples of industrial applications. Contributions are authored by leading academics and practitioners representing various universities, research centres and companies worldwide. Their experience covers multiple disciplines and industries, including finance, construction, logistics, and public services, amongst others.Business Intelligence and Performance Management is a valuable source of reference for graduates approaching MSc or PhD programs and for professionals in industry researching in the fields of BI and PM for industrial application.

RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack maps that aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics are the key topics of this book. The short Python code examples powered by the Java platform can be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell.

This book explores an approach to social robotics based solely on autonomous unsupervised techniques and positions it within a structured exposition of related research in psychology, neuroscience, HRI, and data mining.

In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field.

Artificial Intelligence Techniques for Rational Decision Making

This is the principle puzzle explored in this book where ideas behind systems that behave intelligently are described and different philosophical issues are touched upon. The key to human behavior is taken to be intelligence and the ability to reason about the world.

Data mining essentially relies on several mathematical disciplines, many of which are presented in this second edition of this book. To motivate the reader a significant number of applications of these mathematical tools are included ranging from association rules, clustering algorithms, classification, data constraints, logical data analysis, etc.

Researchers, research students and practitioners in complex systems, artificial intelligence, data science, computer science, and even system science, cognitive science, behaviour science, and social science, will find this book invaluable.

This book examines the application of artificial intelligence methods to model economic data. It addresses causality and proposes new frameworks for dealing with this issue. It also applies evolutionary computing to model evolving economic environments.

This volume offers a scientific approach to manage inter-country conflict. Readers will find that through simultaneous control of four specific aspects (democracy, dependency, allies and capacity), predicted dispute outcomes can be avoided.

Here is an interdisciplinary study of ontology engineering and its application in urban development projects, which includes case studies of the development of ontologies for urban mobility, urban morphological processes, road systems, and cultural heritage.

Optimization techniques have been widely adopted to implement various data mining algorithms. This book focuses on cutting-edge theoretical developments and real-life applications in optimization, covering a range of fields from finance to bioinformatics.

Finding information hidden in data is as theoretically difficult as it is practically important. This book show how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications.

Agent systems are being used to model complex systems like societies, markets and biological systems. This book sheds light upon the complex interrelation between interactivity/exchange and convergence in multi-agent systems, for the first time.

This book details linguistic threshold schemes for information sharing. It examines the opportunities of using these techniques to create new models of managing strategic information shared within a commercial organisation or a state institution.

Written by the primary developer of the jHepWork data analysis framework, this practical book, complete with dozens of code snippets, is a reliable reference source that enables readers to lay the foundation for data-analysis applications using Java scripting.

In addressing the question of how far it is possible to go in knowledge representation and reasoning through graphs, the authors cover basic conceptual graphs, computational aspects, and kernel extensions. The basic mathematical notions are summarized.

This timely text/reference explores the business and technical issues involved in the management of information systems in the era of big data and beyond. describes the key technologies involved in information systems in general and big data in particular, placing these technologies in an historic context;

The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis.

What we profoundly witness these days is a growing number of human-centric systems and a genuine interest in a comprehensive understanding of their underlying paradigms and the development of solid and efficient design practices.

This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques.

This book is the outcome of a decade's research into a speci?c architecture and associated learning mechanism for an arti?cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning.

Evolutionary multiobjective optimization is currently gaining a lot of attention, particularly for researchers in the evolutionary computation communities. Covers the authors' recent research in the area of multiobjective evolutionary algorithms as well as its practical applications.

The well defined model of distributed constraints satisfaction and optimization (DisCSPs/DisCOPs) can serve as the basis for the design and investigation of distributed search algorithms, of protocols and of negotiations and search.

Written from a multidisciplinary perspective, this book supplies an exhaustive description of NKRL and of the associated knowledge representation principles. It also constitutes an invaluable source of reference for practitioners, researchers and graduates.