The book provides a thorough treatment of set functions, games and capacities as well as integrals with respect to capacities and games, in a mathematical rigorous presentation and in view of application to decision making. After a short chapter introducing some required basic knowledge (linear programming, polyhedra, ordered sets) and notation, the first part of the book consists of three long chapters developing the mathematical aspects. This part is not related to a particular application field and, by its neutral mathematical style, is useful to the widest audience. It gathers many results and notions which are scattered in the literature of various domains (game theory, decision, combinatorial optimization and operations research). The second part consists of three chapters, applying the previous notions in decision making and modelling: decision under uncertainty, decision with multiple criteria, possibility theory and Dempster-Shafer theory.

The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancements are supported with illustrative examples and its applications in handling real life problems. The applications are mostly undertaken from real life situations. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing. An elaborate bibliography is provided at the end of each chapter. The material in this book includes concepts, figures, graphs, and tables to guide researchers in the area of big data analysis and cloud computing.

This is the first textbook on attribute exploration, its theory, its algorithms forapplications, and some of its many possible generalizations. Attribute explorationis useful for acquiring structured knowledge through an interactive process, byasking queries to an expert. Generalizations that handle incomplete, faulty, orimprecise data are discussed, but the focus lies on knowledge extraction from areliable information source.The method is based on Formal Concept Analysis, a mathematical theory ofconcepts and concept hierarchies, and uses its expressive diagrams. The presentationis self-contained. It provides an introduction to Formal Concept Analysiswith emphasis on its ability to derive algebraic structures from qualitative data,which can be represented in meaningful and precise graphics.

This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.

'Data Mining Patterns' gives an overall view of the recent solutions for mining and covers mining new kinds of patterns, mining patterns under constraints, new kinds of complex data and real-world applications of these concepts.

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

As information technology continues to advance in massive increments, the bank of information available from personal, financial, and business electronic transactions and all other electronic documentation and data storage is growing at an exponential rate. With this wealth of information comes the opportunity and necessity to utilize this information to maintain competitive advantage and process information effectively in real-world situations. Data Mining and Knowledge Discovery Technologies presents researchers and practitioners in fields such as knowledge management, information science, Web engineering, and medical informatics, with comprehensive, innovative research on data mining methods, structures, tools, and methods, the knowledge discovery process, and data marts, among many other cutting-edge topics.

Data Mining techniques are gradually becoming essential components of corporate intelligence systems and progressively evolving into a pervasive technology within activities that range from the utilization of historical data to predicting the success of an awareness campaign. In reality, data mining is becoming an interdisciplinary field driven by various multi-dimensional applications. Data Mining Applications for Empowering Knowledge Societies presents an overview on the main issues of data mining, including its classification, regression, clustering, and ethical issues. This comprehensive book also provides readers with knowledge enhancing processes as well as a wide spectrum of data mining applications.

The field of enterprise systems integration is constantly evolving, as every new technology that is introduced appears to make all previous ones obsolete. Despite this continuous evolution, there is a set of underlying concepts and technologies that have been gaining an increasing importance in this field. Examples are asynchronous messaging through message queues, data and application adapters based on XML and Web services, the principles associated with the service-oriented architecture (SOA), service composition, orchestrations, and advanced mechanisms such as correlations and long-running transactions. Today, these concepts have reached a significant level of maturity and they represent the foundation over which most integration platforms have been built.

This book addresses integration with a view towards supporting business processes. From messaging systems to data and application adapters, and then to services, orchestrations, and choreographies, the focus is placed on the connection between systems and business processes, and particularly on how it is possible to develop an integrated application infrastructure in order to implement the desired business processes. For this purpose, the text follows a layered, bottom-up approach, with application-oriented integration at the lowest level, followed by service-oriented integration and finally completed by process-oriented integration at the topmost level. The presentation of concepts is accompanied by a set of instructive examples using state-of-the-art technologies such as Java Message Service (JMS), Microsoft Message Queuing (MSMQ), Web Services, Microsoft BizTalk Server, and the Business Process Execution Language (BPEL).

The book is intended as a textbook for advance undergraduate or beginning graduate students in computer science, especially for those in an information systems curriculum. IT professionals with a background in programming, databases and XML will also benefit from the step-by-step description of the various integration levels and the related implementation examples.

Fuzzy Cluster Analysis presents advanced and powerful fuzzy clustering techniques. This thorough and self-contained introduction to fuzzy clustering methods and applications covers classification, image recognition, data analysis and rule generation. Combining theoretical and practical perspectives, each method is analysed in detail and fully illustrated with examples. Features include:

• Sections on inducing fuzzy if-then rules by fuzzy clustering and non-alternating optimization fuzzy clustering algorithms
• Discussion of solid fuzzy clustering techniques like the fuzzy c-means, the Gustafson-Kessel and the Gath-and-Geva algorithm for classification problems
• Focus on linear and shell clustering techniques used for detecting contours in image analysis
• Accompanying software and data sets pertaining to the examples presented, enabling the reader to learn through experimentation
• Examination of the difficulties involved in evaluating the results of fuzzy cluster analysis and of determining the number of clusters with analysis of global and local validity measures
• Description of different fuzzy clustering techniques allowing the user to select the method most appropriate to a particular problem

The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data's immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data. The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments.

When digitized entities, connected devices and microservices interact purposefully, we end up with a massive amount of multi-structured streaming (real-time) data that is continuously generated by different sources at high speed. Streaming analytics allows the management, monitoring, and real-time analytics of live streaming data. The topic has grown in importance due to the emergence of online analytics and edge and IoT platforms. A real digital transformation is being achieved across industry verticals through meticulous data collection, cleansing and crunching in real time. Capturing and subjecting those value-adding events is considered to be the prime task for achieving trustworthy and timely insights. The authors articulate and accentuate the challenges widely associated with streaming data and analytics, describe data analytics algorithms and approaches, present edge and fog computing concepts and technologies and show how streaming analytics can be accomplished in edge device clouds. They also delineate several industry use cases across cloud system operations in transportation and cyber security and other business domains. The book will be of interest to ICTs industry and academic researchers, scientists and engineers as well as lecturers and advanced students in the fields of data science, cloud/fog/edge architecture, internet of things and artificial intelligence and related fields of applications. It will also be useful to cloud/edge/fog and IoT architects, analytics professionals, IT operations teams and site reliability engineers (SREs).

Customers and products are the heart of any business, and corporations collect more data about them every year. However, just because you have data doesn t mean you can use it effectively. If not properly integrated, data can actually encourage false conclusions that result in bad decisions and lost opportunities. Entity Resolution (ER) is a powerful tool for transforming data into accurate, value-added information. Using entity resolution methods and techniques, you can identify equivalent records from multiple sources corresponding to the same real-world person, place, or thing.

This emerging area of data management is clearly explained throughout the book. It teaches you the process of locating and linking information about the same entity - eliminating duplications - and making crucial business decisions based on the results. This book is an authoritative, vendor-independent technical reference for researchers, graduate students and practitioners, including architects, technical analysts, and solution developers. In short, Entity Resolution and Information Quality gives you the applied level know-how you need to aggregate data from disparate sources and form accurate customer and product profiles that support effective marketing and sales. It is an invaluable guide for succeeding in today s info-centric environment.
First authoritative reference explaining entity resolution and how to use it effectivelyProvides practical system design advice to help you get a competitive advantage Includes a companion site with synthetic customer data for applicatory exercises, and access to a Java-based Entity Resolution program. "

As the amount of accumulated data across a variety of fields becomes harder to maintain, it is essential for a new generation of computational theories and tools to assist humans in extracting knowledge from this rapidly growing digital data. Global Trends in Intelligent Computing Research and Development brings together recent advances and in depth knowledge in the fields of knowledge representation and computational intelligence. Highlighting the theoretical advances and their applications to real life problems, this book is an essential tool for researchers, lecturers, professors, students, and developers who have seek insight into knowledge representation and real life applications.

This book documents recent attempts to conduct systematic, prodigious and multidisciplinary research in learning analytics and present their findings and identify areas for further research and development. The book also unveils the distinguished and exemplary works by educators and researchers in the field highlighting the current trends, privacy and ethical issues, creative and unique approaches, innovative methods, frameworks, and theoretical and practical aspects of learning analytics.

This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox to improve understanding and exploit business opportunities related to Big Data and analytics.

The problem of mining patterns is becoming a very active research area and efficient techniques have been widely applied to problems in industry, government, and science. From the initial definition and motivated by real-applications, the problem of mining patterns not only addresses the finding of itemsets but also more and more complex patterns.

Disaster management is a process or strategy that is implemented when any type of catastrophic event takes place. The process may be initiated when anything threatens to disrupt normal operations or puts the lives of human beings at risk. Governments on all levels as well as many businesses create some sort of disaster plan that make it possible to overcome the catastrophe and return to normal function as quickly as possible. Response to natural disasters (e.g., floods, earthquakes) or technological disaster (e.g., nuclear, chemical) is an extreme complex process that involves severe time pressure, various uncertainties, high non-linearity and many stakeholders. Disaster management often requires several autonomous agencies to collaboratively mitigate, prepare, respond, and recover from heterogeneous and dynamic sets of hazards to society. Almost all disasters involve high degrees of novelty to deal with most unexpected various uncertainties and dynamic time pressures. Existing studies and approaches within disaster management have mainly been focused on some specific type of disasters with certain agency oriented. There is a lack of a general framework to deal with similarities and synergies among different disasters by taking their specific features into account. This book provides with various decisions analysis theories and support tools in complex systems in general and in disaster management in particular. The book is also generated during a long-term preparation of a European project proposal among most leading experts in the areas related to the book title. Chapters are evaluated based on quality and originality in theory and methodology, application oriented, relevance to the title of the book.

This book will help organizations who have implemented or are considering implementing Microsoft Dynamics achieve a better result.

It presents Regatta Dynamics, a methodology developed by the authors for the structured implementation of Microsoft Dynamics. From A-to-Z, it details the full implementation process, emphasizing the organizational component of the implementation process and the cohesion with functional and technical processes.

This book is a tribute to Professor Jacek Zurada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Zurada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of Louisville, Kentucky, USA, as a token of appreciation for his scientific and scholarly achievements, and for his longstanding service to many communities, notably the computational intelligence community, in particular neural networks, machine learning, data analyses and data mining, but also the fuzzy logic and evolutionary computation communities, to name but a few. At the same time, the book recognizes and honors Professor Zurada's dedication and service to many scientific, scholarly and professional societies, especially the IEEE (Institute of Electrical and Electronics Engineers), the world's largest professional technical professional organization dedicated to advancing science and technology in a broad spectrum of areas and fields. The volume is divided into five major parts, the first of which addresses theoretic, algorithmic and implementation problems related to the intelligent use of data in the sense of how to derive practically useful information and knowledge from data. In turn, Part 2 is devoted to various aspects of neural networks and connectionist systems. Part 3 deals with essential tools and techniques for intelligent technologies in systems modeling and Part 4 focuses on intelligent technologies in decision-making, optimization and control, while Part 5 explores the applications of intelligent technologies.

The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.

Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data. Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.

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.