This book presents new approaches that advance research in all aspects of agent-based models, technologies, simulations and implementations for data intensive applications. The nine chapters contain a review of recent cross-disciplinary approaches in cloud environments and multi-agent systems, and important formulations of data intensive problems in distributed computational environments together with the presentation of new agent-based tools to handle those problems and Big Data in general. This volume can serve as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary work in the areas of data intensive computing and Big Data systems using emergent large-scale distributed computing paradigms. It will also allow newcomers to grasp key concepts and potential solutions on advanced topics of theory, models, technologies, system architectures and implementation of applications in Multi-Agent systems and data intensive computing.

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.

Prevalent types of data in scientific visualization are volumetric data, vector field data, and particle-based data. Particle data typically originates from measurements and simulations in various fields, such as life sciences or physics. The particles are often visualized directly, that is, by simple representants like spheres. Interactive rendering facilitates the exploration and visual analysis of the data. With increasing data set sizes in terms of particle numbers, interactive high-quality visualization is a challenging task. This is especially true for dynamic data or abstract representations that are based on the raw particle data. This book covers direct particle visualization using simple glyphs as well as abstractions that are application-driven such as clustering and aggregation. It targets visualization researchers and developers who are interested in visualization techniques for large, dynamic particle-based data. Its explanations focus on GPU-accelerated algorithms for high-performance rendering and data processing that run in real-time on modern desktop hardware. Consequently, the implementation of said algorithms and the required data structures to make use of the capabilities of modern graphics APIs are discussed in detail. Furthermore, it covers GPU-accelerated methods for the generation of application-dependent abstract representations. This includes various representations commonly used in application areas such as structural biology, systems biology, thermodynamics, and astrophysics.

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.

Introduces an assortment of powerful command line utilities that can be combined to create simple, yet powerful shell scripts for processing datasets. The code samples and scripts use the bash shell, and typically involve small datasets so you can focus on understanding the features of grep, sed, and awk. Companion files with code are available for downloading from the publisher.

This book comprises a set of articles that specify the methodology of text mining, describe the creation of lexical resources in the framework of text mining and use text mining for various tasks in natural language processing (NLP). The analysis of large amounts of textual data is a prerequisite to build lexical resources such as dictionaries and ontologies and also has direct applications in automated text processing in fields such as history, healthcare and mobile applications, just to name a few. This volume gives an update in terms of the recent gains in text mining methods and reflects the most recent achievements with respect to the automatic build-up of large lexical resources. It addresses researchers that already perform text mining, and those who want to enrich their battery of methods. Selected articles can be used to support graduate-level teaching. The book is suitable for all readers that completed undergraduate studies of computational linguistics, quantitative linguistics, computer science and computational humanities. It assumes basic knowledge of computer science and corpus processing as well as of statistics.

This book constitutes the proceedings of the 14th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2016, held in Phuket, Thailand, in August 2016. The 16 full papers and 5 short papers included in this volume were carefully reviewed and selected from 61 initial submissions. They deal with knowledge acquisition and machine learning; knowledge acquisition and natural language processing; knowledge acquisition from network and big data; and knowledge acquisition 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 constitutes the refereed proceedings of the 15th International Conference on Systems Simulation, Asia Simulation 2015, held in Jeju, Korea, in November 2016.The 11 revised full papers presented were carefully reviewed and selected from 126 submissions. The papers are organized in topical sections on model and design; simulation and analysis.

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 discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation. This methodology affords user interaction on visual data representations that are native to the domain of the data. User interaction in visual analytics systems is critical to enabling visual data exploration. Interaction transforms people from mere viewers to active participants in the process of analyzing and understanding data. This discourse between people and data enables people to understand aspects of their data, such as structure, patterns, trends, outliers, and other properties that ultimately result in insight. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. The book provides a description of the principles of semantic interaction, providing design guidelines for the integration of semantic interaction into visual analytics, examples of existing technologies that leverage semantic interaction, and a discussion of how to evaluate these technologies. Semantic interaction has the potential to increase the effectiveness of visual analytic technologies and opens possibilities for a fundamentally new design space for user interaction in visual analytics systems.

This book offers a thorough yet easy-to-read reference guide to various aspects of cloud computing security. It begins with an introduction to the general concepts of cloud computing, followed by a discussion of security aspects that examines how cloud security differs from conventional information security and reviews cloud-specific classes of threats and attacks. A range of varying threats in cloud computing are covered, from threats of data loss and data breaches, to threats to availability and threats posed by malicious insiders. Further, the book discusses attacks launched on different levels, including attacks on the hypervisor, and on the confidentiality of data. Newer types, such as side-channel attacks and resource-freeing attacks, are also described. The work closes by providing a set of general security recommendations for the cloud.

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.

Learn SQL Server Reporting Services and become current with the 2016 edition. Develop interactive, dynamic reports that combine graphs, charts, and tabular data into attractive dashboards and reports to delight business analysts and other users of corporate data. Deliver mobile reports to anywhere and any device. Build vital knowledge of Reporting Services at a time when Microsoft's dominance in business intelligence is on the rise. Beginning SQL Server Reporting Services turns novices into skilled report developers. The book begins by explaining how to set up the development environment. It then walks you through creating your first reports using the built-in wizard. After showing what is possible, the book breaks down and explains the skills needed to create reports from scratch. And not just reports! But also dashboards with charts, graphs, and maps. Each chapter builds on knowledge gained in the previous chapters with step-by-step tutorials. Beginning SQL Server Reporting Services boosts your skills and provides you additional career options. Don't be without those options. Grab and read this book today. Build reports with and without the built-in wizard. Build interactive features such as drill-through reports. Build dashboards full of charts, graphs, and maps. Build mobile reports. What You Will Learn Set up your development environment. Organized projects and share components among reports. Create report using a wizard. Create reports from scratch, including grouping levels, parameters, and drill through features. Build interactive dashboard with graphs, charts, and maps. Deploy and manage reports for use by others in the business. Who This Book Is For Database professionals of all experience levels who have some experience in databases and want to make the leap into business intelligence reporting. The book is an excellent choice for those needing to add Reporting Services to their current list of skills, or who are looking for a skill set that is in demand for in order to break into IT.

This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data. The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms. They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers. The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.

This book reports on cutting-edge research carried out within the context of the EU-funded Dicode project, which aims at facilitating and augmenting collaboration and decision making in data-intensive and cognitively complex settings. Whenever appropriate, Dicode builds on prominent high-performance computing paradigms and large data processing technologies to meaningfully search, analyze, and aggregate data from diverse, extremely large and rapidly evolving sources. The Dicode approach and services are fully explained and particular emphasis is placed on deepening insights regarding the exploitation of big data, as well as on collaboration and issues relating to sense-making support. Building on current advances, the solution developed in the Dicode project brings together the reasoning capabilities of both the machine and humans. It can be viewed as an innovative "workbench" incorporating and orchestrating a set of interoperable services that reduce the data intensiveness and complexity overload at critical decision points to a manageable level, thus permitting stakeholders to be more productive and effective in their work practices.

This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets and the importance of interpreting displays effectively is emphasized. Graphics should be drawn to convey information and the book includes many insightful examples. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. The book is accessible to readers with some experience of drawing statistical graphics.

The development of Operations Research (OR) requires constant improvements, such as the integration of research results with business applications and innovative educational practice. The full deployment and commercial exploitation of goods and services generally need the construction of strong synergies between educational institutions and businesses. The IO2015 -XVII Congress of APDIO aims at strengthening the knowledge triangle in education, research and innovation, in order to maximize the contribution of OR for sustainable growth, the promoting of a knowledge-based economy, and the smart use of finite resources. The IO2015-XVII Congress of APDIO is a privileged meeting point for the promotion and dissemination of OR and related disciplines, through the exchange of ideas among teachers, researchers, students , and professionals with different background, but all sharing a common desire that is the development of OR.

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Advance Praise for Indian Mujahideen: Computational Analysis and Public Policy "This book presents a highly innovative computational approach to analyzing the strategic behavior of terrorist groups and formulating counter-terrorism policies. It would be very useful for international security analysts and policymakers." Uzi Arad, National Security Advisor to the Prime Minister of Israel and Head, Israel National Security Council (2009-2011) "An important book on a complex security problem. Issues have been analysed in depth based on quality research. Insightful and well-balanced in describing the way forward." Naresh Chandra, Indian Ambassador to the USA (1996-2001) and Cabinet Secretary (1990-1992). "An objective and clinical account of the origins, aims, extra-territorial links and modus-operandi, of a growingly dangerous terrorist organization that challenges the federal, democratic, secular and pluralistic ethos of India's polity. The authors have meticulously researched and analysed the multi-faceted challenges that the "Indian Mujahideen" poses and realistically dwelt on the ways in which these challenges could be faced and overcome." G. Parthasarathy, High Commissioner of India to Australia (1995-1998) and Pakistan (1998-2000). This book provides the first in-depth look at how advanced mathematics and modern computing technology can influence insights on analysis and policies directed at the Indian Mujahideen (IM) terrorist group. The book also summarizes how the IM group is committed to the destabilization of India by leveraging links with other terror groups such as Lashkar-e-Taiba, and through support from the Pakistani Government and Pakistan's intelligence service. Foreword by The Hon. Louis J. Freeh.

Statistical Decision Problems presents a quick and concise introduction into the theory of risk, deviation and error measures that play a key role in statistical decision problems. It introduces state-of-the-art practical decision making through twenty-one case studies from real-life applications. The case studies cover a broad area of topics and the authors include links with source code and data, a very helpful tool for the reader. In its core, the text demonstrates how to use different factors to formulate statistical decision problems arising in various risk management applications, such as optimal hedging, portfolio optimization, cash flow matching, classification, and more. The presentation is organized into three parts: selected concepts of statistical decision theory, statistical decision problems, and case studies with portfolio safeguard. The text is primarily aimed at practitioners in the areas of risk management, decision making, and statistics. However, the inclusion of a fair bit of mathematical rigor renders this monograph an excellent introduction to the theory of general error, deviation, and risk measures for graduate students. It can be used as supplementary reading for graduate courses including statistical analysis, data mining, stochastic programming, financial engineering, to name a few. The high level of detail may serve useful to applied mathematicians, engineers, and statisticians interested in modeling and managing risk in various applications.

This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.

This book constitutes the thoroughly refereed short papers, workshops and Doctoral Consortium papers of the 20th East European Conference on Advances in Databases and Information Systems, ADBIS 2016, held in Prague, Czech Republic, in August 2016. The 11 short papers and one historical paper were carefully selected and reviewed from 85 submissions. The rest of papers was selected from reviewing processes of 2 workshops and Doctoral Consortium. The papers are organized in topical sections on ADBIS Short Papers, Third International Workshop on Big Data Applications and Principles (BigDap 2016), Second International Workshop on Data Centered Smart Applications (DCSA 2016) and ADBIS Doctoral Consortium.