This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors' website.

The book reports on the latest advances and challenges of soft computing. It gathers original scientific contributions written by top scientists in the field and covering theories, methods and applications in a number of research areas related to soft-computing, such as decision-making, probabilistic reasoning, image processing, control, neural networks and data analysis.

Social network analysis increasingly bridges the discovery of patterns in diverse areas of study as more data becomes available and complex. Yet the construction of huge networks from large data often requires entirely different approaches for analysis including; graph theory, statistics, machine learning and data mining. This work covers frontier studies on social network analysis and mining from different perspectives such as social network sites, financial data, e-mails, forums, academic research funds, XML technology, blog content, community detection and clique finding, prediction of user's- behavior, privacy in social network analysis, mobility from spatio-temporal point of view, agent technology and political parties in parliament. These topics will be of interest to researchers and practitioners from different disciplines including, but not limited to, social sciences and engineering.

This book reports on new theories and applications in the field of intelligent systems and computing. It covers computational and artificial intelligence methods, as well as advances in computer vision, current issues in big data and cloud computing, computation linguistics, and cyber-physical systems. It also reports on data mining and knowledge extraction technologies, as well as central issues in intelligent information management. Written by active researchers, the respective chapters are based on papers presented at the International Conference on Computer Science and Information Technologies (CSIT 2017), held on September 5-8, 2017, in Lviv, Ukraine; and at two workshops accompanying the conference: one on inductive modeling, jointly organized by the Lviv Polytechnic National University and the National Academy of Science of Ukraine; and another on project management, which was jointly organized by the Lviv Polytechnic National University, the International Project Management Association, the Ukrainian Project Management Association, the Kazakhstan Project Management Association, and Nazarbayev University. Given its breadth of coverage, the book provides academics and professionals with extensive information and a timely snapshot of the field of intelligent systems, and is sure to foster new discussions and collaborations among different groups.

This book comprises select proceedings of the 2015 annual conference of the Computer Society of India. The books focuses on next generation networks (NGN). An NGN is a packet-based network which can provide services including telecommunication services. NGNs make use of multiple broadband, quality-of-service-enabled transport technologies in which service-related functions are independent from underlying transport-related technologies. This volume includes contributions from experts on various aspects of NGNs. The papers included cover theory, methodology and applications of ad-hoc networks, sensor networks, and the internet. The contents also delve into how the new enterprise IT landscape of cloud services, mobility, social media usage and big data analytics creates different types of network traffic to the traditional mix of in-house client-server enterprise workloads. The contents of this book will be useful to researchers and professionals alike.

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field. The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections: Supplies a complete overview of the evolution of the field and its intersection with computational learning Describes the role of data mining in analyzing large biological databases-explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data Covers supervised learning using classification techniques most commonly used in bioinformatics-addressing the need for validation and benchmarking of inferences derived using either clustering or classification The book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biologica

This book reports on the development and validation of a generic defeasible logic programming framework for carrying out argumentative reasoning in Semantic Web applications (GF@SWA). The proposed methodology is unique in providing a solution for representing incomplete and/or contradictory information coming from different sources, and reasoning with it. GF@SWA is able to represent this type of information, perform argumentation-driven hybrid reasoning to resolve conflicts, and generate graphical representations of the integrated information, thus assisting decision makers in decision making processes. GF@SWA represents the first argumentative reasoning engine for carrying out automated reasoning in the Semantic Web context and is expected to have a significant impact on future business applications. The book provides the readers with a detailed and clear exposition of different argumentation-based reasoning techniques, and of their importance and use in Semantic Web applications. It addresses both academics and professionals, and will be of primary interest to researchers, students and practitioners in the area of Web-based intelligent decision support systems and their application in various domains.

This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.

This volume addresses the emerging area of human computation, The chapters, written by leading international researchers, explore existing and future opportunities to combine the respective strengths of both humans and machines in order to create powerful problem-solving capabilities. The book bridges scientific communities, capturing and integrating the unique perspective and achievements of each. It coalesces contributions from industry and across related disciplines in order to motivate, define, and anticipate the future of this exciting new frontier in science and cultural evolution. Readers can expect to find valuable contributions covering Foundations; Application Domains; Techniques and Modalities; Infrastructure and Architecture; Algorithms; Participation; Analysis; Policy and Security and the Impact of Human Computation. Researchers and professionals will find the Handbook of Human Computation a valuable reference tool. The breadth of content also provides a thorough foundation for students of the field.

This book constitutes the refereed proceedings of the 17th International Symposium, KSS 2016, held in Kobe, Japan, in November 2016. The 21 revised full papers presented were carefully reviewed and selected from 48 submissions. The papers cover topics such as: Algorithms for Big Data; Big Data and education; Big Data and healthcare; Big Data and tourism; Big Data and social media oriented knowledge discovery and data mining, text mining, recommendation system, etc; Big Data, social media and societal management; creation of agent-based social systems sciences; collective intelligence; complex system modeling and complexity; decision analysis and decision support systems; internet+ and agriculture; internet+ and open innovation; knowledge creation, creativity support, awareness support, etc.; knowledge systems engineering and knowledge management; meta-synthesis and advanced modeling; opinion dynamics and opinion mining; OR on knowledge and systems sciences; problem structuring methods and system methodologies toward wicked problems; service systems science; smart city; social dynamic network modeling; Web intelligence.

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.

Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore behavioral questions in information systems research. As adoption and use of these research methods expand, there is growing need for a resource book to assist doctoral students and advanced researchers in understanding their potential to contribute to a broad range of research problems. Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking focuses on bridging and unifying these three different methodologies in order to bring them together in a unified volume for the information systems community. This book serves as a resource that provides overviews on each method, as well as applications on how they can be employed to address IS research problems. Its goal is to help researchers in their continuous efforts to set the pace for having an appropriate interplay between behavioral research and design science.

This book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and end-to-end examples for publishing datasets as Linked Data, and use cases that harness scholarly information and sensor data. It presents how Linked Data is used for web-scale data integration, information management and search. Special emphasis is given to the publication of Linked Data from relational databases as well as from real-time sensor data streams. The authors also trace the transformation from the document-based World Wide Web into a Web of Data. Materializing the Web of Linked Data is addressed to researchers and professionals studying software technologies, tools and approaches that drive the Linked Data ecosystem, and the Web in general.

This book provides a timely and unique survey of next-generation social computational methodologies. The text explains the fundamentals of this field, and describes state-of-the-art methods for inferring social status, relationships, preferences, intentions, personalities, needs, and lifestyles from human information in unconstrained visual data. Topics and features: includes perspectives from an international and interdisciplinary selection of pre-eminent authorities; presents balanced coverage of both detailed theoretical analysis and real-world applications; examines social relationships in human-centered media for the development of socially-aware video, location-based, and multimedia applications; reviews techniques for recognizing the social roles played by people in an event, and for classifying human-object interaction activities; discusses the prediction and recognition of human attributes via social media analytics, including social relationships, facial age and beauty, and occupation.

Information Systems (IS) as a discipline draws on diverse areas including, technology, organisational theory, management and social science. The field is recognized as very broad and encompassing many themes and areas. However, the development of artefacts, or information systems development (ISD), in the broadest sense, is a central concern of the discipline. Significantly, ISD impacts on the organisational and societal contexts through the use of the artefacts constructed by the development. Today, that impact also needs to be evaluated in terms of its effects on the environment. Sustainable, or "green," IT is a catch-all term used to describe the development, manufacture, management, use and disposal of ICT in a way that minimizes damage to the environment. As a result, the term has many different meanings, depending on the role assumed in the life span of the ICT artefact. The theme of the proposed work is to critically examine the whole range of issues around ISD from the perspective of sustainability. Sustainable IT is an emerging theme in academic research and industry practice in response to an individual concern for the environment and the embryonic regulatory environments being enacted globally to address the environmental impact of ICT. In this work we intend to bring together in one volume the diverse research around the development of sustainable IS.

Proper analysis of image and multimedia data requires efficient extraction and segmentation techniques. Among the many computational intelligence approaches, the soft computing paradigm is best equipped with several tools and techniques that incorporate intelligent concepts and principles. This book is dedicated to object extraction, image segmentation, and edge detection using soft computing techniques with extensive real-life application to image and multimedia data. The authors start with a comprehensive tutorial on the basics of brain structure and learning, and then the key soft computing techniques, including evolutionary computation, neural networks, fuzzy sets and fuzzy logic, and rough sets. They then present seven chapters that detail the application of representative techniques to complex image processing tasks such as image recognition, lighting control, target tracking, object extraction, and edge detection. These chapters follow a structured approach with detailed explanations of the problems, solutions, results, and conclusions. This is both a standalone textbook for graduates in computer science, electrical engineering, system science, and information technology, and a reference for researchers and engineers engaged with pattern recognition, image processing, and soft computing.

This unique text/reference describes an exciting and novel approach to supercomputing in the DataFlow paradigm. The major advantages and applications of this approach are clearly described, and a detailed explanation of the programming model is provided using simple yet effective examples. The work is developed from a series of lecture courses taught by the authors in more than 40 universities across more than 20 countries, and from research carried out by Maxeler Technologies, Inc. Topics and features: presents a thorough introduction to DataFlow supercomputing for big data problems; reviews the latest research on the DataFlow architecture and its applications; introduces a new method for the rapid handling of real-world challenges involving large datasets; provides a case study on the use of the new approach to accelerate the Cooley-Tukey algorithm on a DataFlow machine; includes a step-by-step guide to the web-based integrated development environment WebIDE.

This book constitutes the refereed proceedings of the 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, held in Edinburgh, UK, in July 2017. The 82 revised full papers presented were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on retinal imaging, ultrasound imaging, cardiovascular imaging, oncology imaging, mammography image analysis, image enhancement and alignment, modeling and segmentation of preclinical, body and histological imaging, feature detection and classification. The chapters 'Model-Based Correction of Segmentation Errors in Digitised Histological Images' and 'Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering' are open access under a CC BY 4.0 license.

This book constitutes the post-conference proceedings of the Third International Workshop on Machine Learning, Optimization, and Big Data, MOD 2017, held in Volterra, Italy, in September 2017. The 50 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.

This book provides a comprehensive overview on emergent bursty patterns in the dynamics of human behaviour. It presents common and alternative understanding of the investigated phenomena, and points out open questions worthy of further investigations. The book is structured as follows. In the introduction the authors discuss the motivation of the field, describe bursty phenomena in case of human behaviour, and relate it to other disciplines. The second chapter addresses the measures commonly used to characterise heterogeneous signals, bursty human dynamics, temporal paths, and correlated behaviour. These definitions are first introduced to set the basis for the discussion of the third chapter about the observations of bursty human patterns in the dynamics of individuals, dyadic interactions, and collective behaviour. The subsequent fourth chapter discusses the models of bursty human dynamics. Various mechanisms have been proposed about the source of the heterogeneities in human dynamics, which leads to the introduction of conceptually different modelling approaches. The authors address all of these perspectives objectively, highlight their strengths and shortcomings, and mention possible extensions to them. The fifth chapter addresses the effect of individual heterogeneous behaviour on collective dynamics. This question in particular has been investigated in various systems including spreading phenomena, random walks, and opinion formation dynamics. Here the main issues are whether burstiness speeds up or slows down the co-evolving processes, and how burstiness modifies time-dependent paths in the system that determine the spreading patterns of any kind of information or influence. Finally in the sixth chapter the authors end the review with a discussion and future perspectives. It is an ideal book for researchers and students who wish to enter the field of bursty human dynamics or want to expand their knowledge on such phenomena.

This, the 27th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of 12 papers presented at the Big Data and Technology for Complex Urban Systems symposium, held in Kauai, HI, USA in January 2016. The papers explore the use of big data in complex urban systems in the areas of politics, society, commerce, tax, and emergency management.

In this book, the authors describe how Mind Genomics works - a revolutionary marketing method that combines the three sciences of Mathematics, Psychology, and Economics - in a masterful way. Mind Genomics helps the seller of products and services to know what people are thinking about them before one ever commits to an approach by knowing what is important to the people one is trying to influence. Mind Genomics identifies what aspects of a general topic are important to the audience, how different people in the audience will respond to different aspects of that topic, and how to pinpoint the viewpoints of different audience segments to each aspect of the topic. A careful step by step approach explains what activities ought to be taken and what scenarios must be followed while applying this method in order to find the right way to capture the hearts and minds of targeted audiences. This book explains how Mind Genomics plays a matching game with one's potential audience and various ways one can present the products and ideas resulting in a systematic approach to influencing others, backed by real data; how one can play with ideas, see patterns imposed by the mind and create new, inductive, applied sciences of the mind, measuring the world using the mind of man as the yardstick. In details it describes how everyday thought is transferred into actionable data and results. Whether one is a senior marketer for a large corporation, a professor at a university, or administrator at a hospital, one could use Mind Genomics to learn how to transform available information into actionable steps that will increase the products sales, or increase the number of interested students for a new university program, or the number of satisfied patients in the hospital with their medical conditions kept at highest levels after leaving it. Mind Genomics was first introduced by Dr. Howard Moskowitz, an alumnus of Harvard University and the father of Horizontal Segmentation - a widely accepted business model for targeted marketing and profit maximization.

This book constitutes the refereed proceedings of the 6th International Conference on Well-Being in the Information Society, WIS 2016, held in Tampere, Finland, in September 2016. The 21 revised full papers presented were carefully reviewed and selected from 42 submissions. With the core topic "Building Sustainable Health Ecosystems" WIS 2016 focused on innovations and fresh ideas in the cross-section of urban living, information society and health as understood in a wide sense. The papers presented in this volume are organized along the following seven broad topics: 1. Macro level considerations of e-health and welfare, 2.Welfare issues of children, youth, young elderly and seniors, 3. Analytics issues of eHealth and welfare, 4. National/regional initiatives in eHealth and welfare, and 5. Specific topics of eHealth. The papers in these topics span qualitative and quantitative analysis, empirical surveys, case studies as well as conceptual work.

A broad spectrum of modern Information Technology (IT) tools, techniques, main developments and still open challenges is presented. Emphasis is on new research directions in various fields of science and technology that are related to data analysis, data mining, knowledge discovery, information retrieval, clustering and classification, decision making and decision support, control, computational mathematics and physics, to name a few. Applications in many relevant fields are presented, notably in telecommunication, social networks, recommender systems, fault detection, robotics, image analysis and recognition, electronics, etc. The methods used by the authors range from high level formal mathematical tools and techniques, through algorithmic and computational tools, to modern metaheuristics.