This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.

This guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it. Joanne Rodrigues-Craig bridges the gap between predictive data science and statistical techniques that reveal why important things happen -- why customers buy more, or why they immediately leave your site -- so you can get more behaviors you want and less you don't. Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues-Craig shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behavior, and earning business value. You'll learn how to: Develop complex, testable theories for understanding individual and social behavior in web products Think like a social scientist and contextualize individual behavior in today's social environments Build more effective metrics and KPIs for any web product or system Conduct more informative and actionable A/B tests Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation Alter user behavior in a complex web product Understand how relevant human behaviors develop, and the prerequisites for changing them Choose the right statistical techniques for common tasks such as multistate and uplift modeling Use advanced statistical techniques to model multidimensional systems Do all of this in R (with sample code available in a separate code manual)

This book introduces the latest thinking on the use of Big Data in the context of urban systems, including research and insights on human behavior, urban dynamics, resource use, sustainability and spatial disparities, where it promises improved planning, management and governance in the urban sectors (e.g., transportation, energy, smart cities, crime, housing, urban and regional economies, public health, public engagement, urban governance and political systems), as well as Big Data's utility in decision-making, and development of indicators to monitor economic and social activity, and for urban sustainability, transparency, livability, social inclusion, place-making, accessibility and resilience.

The book provides an overview of the state-of-the-art of map construction algorithms, which use tracking data in the form of trajectories to generate vector maps. The most common trajectory type is GPS-based trajectories. It introduces three emerging algorithmic categories, outlines their general algorithmic ideas, and discusses three representative algorithms in greater detail. To quantify map construction algorithms, the authors include specific datasets and evaluation measures. The datasets, source code of map construction algorithms and evaluation measures are publicly available on http://www.mapconstruction.org. The web site serves as a repository for map construction data and algorithms and researchers can contribute by uploading their own code and benchmark data. Map Construction Algorithms is an excellent resource for professionals working in computational geometry, spatial databases, and GIS. Advanced-level students studying computer science, geography and mathematics will also find this book a useful tool.

This book explores a broad cross section of research and actual case studies to draw out new insights that may be used to build a benchmark for IT security professionals. This research takes a deeper dive beneath the surface of the analysis to uncover novel ways to mitigate data security vulnerabilities, connect the dots and identify patterns in the data on breaches. This analysis will assist security professionals not only in benchmarking their risk management programs but also in identifying forward looking security measures to narrow the path of future vulnerabilities.

This handbook provides an overarching view of cyber security and digital forensic challenges related to big data and IoT environment, prior to reviewing existing data mining solutions and their potential application in big data context, and existing authentication and access control for IoT devices. An IoT access control scheme and an IoT forensic framework is also presented in this book, and it explains how the IoT forensic framework can be used to guide investigation of a popular cloud storage service. A distributed file system forensic approach is also presented, which is used to guide the investigation of Ceph. Minecraft, a Massively Multiplayer Online Game, and the Hadoop distributed file system environment are also forensically studied and their findings reported in this book. A forensic IoT source camera identification algorithm is introduced, which uses the camera's sensor pattern noise from the captured image. In addition to the IoT access control and forensic frameworks, this handbook covers a cyber defense triage process for nine advanced persistent threat (APT) groups targeting IoT infrastructure, namely: APT1, Molerats, Silent Chollima, Shell Crew, NetTraveler, ProjectSauron, CopyKittens, Volatile Cedar and Transparent Tribe. The characteristics of remote-controlled real-world Trojans using the Cyber Kill Chain are also examined. It introduces a method to leverage different crashes discovered from two fuzzing approaches, which can be used to enhance the effectiveness of fuzzers. Cloud computing is also often associated with IoT and big data (e.g., cloud-enabled IoT systems), and hence a survey of the cloud security literature and a survey of botnet detection approaches are presented in the book. Finally, game security solutions are studied and explained how one may circumvent such solutions. This handbook targets the security, privacy and forensics research community, and big data research community, including policy makers and government agencies, public and private organizations policy makers. Undergraduate and postgraduate students enrolled in cyber security and forensic programs will also find this handbook useful as a reference.

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.

If you are a manager who receives the results of any data analyst's work to help with your decision-making, this book is for you. Anyone playing a role in the field of analytics can benefit from this book as well. In the two decades the editors of this book spent teaching and consulting in the field of analytics, they noticed a critical shortcoming in the communication abilities of many analytics professionals. Specifically, analysts have difficulty in articulating in business terms what their analyses showed and what actionable recommendations were made. When analysts made presentations, they tended to lapse into the technicalities of mathematical procedures, rather than focusing on the strategic and tactical impact and meaning of their work. As analytics has become more mainstream and widespread in organizations, this problem has grown more acute. Data Analytics: Effective Methods for Presenting Results tackles this issue. The editors have used their experience as presenters and audience members who have become lost during presentation. Over the years, they experimented with different ways of presenting analytics work to make a more compelling case to top managers. They have discovered tried and true methods for improving presentations, which they share. The book also presents insights from other analysts and managers who share their own experiences. It is truly a collection of experiences and insight from academics and professionals involved with analytics. The book is not a primer on how to draw the most beautiful charts and graphs or about how to perform any specific kind of analysis. Rather, it shares the experiences of professionals in various industries about how they present their analytics results effectively. They tell their stories on how to win over audiences. The book spans multiple functional areas within a business, and in some cases, it discusses how to adapt presentations to the needs of audiences at different levels of management.

Data Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship. The FAIR principles are used as a guide throughout the text, and this book should leave experimentalists consciously incompetent about data stewardship and motivated to respect data stewards as representatives of a new profession, while possibly motivating others to consider a career in the field. The ebook, avalable for no additional cost when you buy the paperback, will be updated every 6 months on average (providing that significant updates are needed or avaialble). Readers will have the opportunity to contribute material towards these updates, and to develop their own data management plans, via the free Data Stewardship Wizard.

This book is about the process of using analytics and the capabilities of analytics in today's organizations. Cutting through the buzz surrounding the term analytics and the overloaded expectations about using analytics, the book demystifies analytics with an in-depth examination of concepts grounded in operations research and management science. Analytics as a set of tools and processes is only as effective as: The data with which it is working The human judgment applying the processes and understanding the output of these processes. For this reason, the book focuses on the analytics process. What is intrinsic to analytics' real organizational impact are the careful application of tools and the thoughtful application of their outcomes. This work emphasizes analytics as part of a process that supports decision-making within organizations. It wants to debunk overblown expectations that somehow analytics outputs or analytics as applied to other concepts, such as Big Data, are the be-all and end-all of the analytics process. They are, instead, only a step within a holistic and critical approach to management thinking that can create real value for an organization. To develop this holistic approach, the book is divided into two sections that examine concepts and applications. The first section makes the case for executive management taking a holistic approach to analytics. It draws on rich research in operations and management science that form the context in which analytics tools are to be applied. There is a strong emphasis on knowledge management concepts and techniques, as well as risk management concepts and techniques. The second section focuses on both the use of the analytics process and organizational issues that are required to make the analytics process relevant and impactful.

This book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided. The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.

The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience. As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student' s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions to improve, the learning process of the student. Third, there are possibilities in using data analytics to measure the performance of instructors. Finally, for policy makers, it is often unclear how schools use their available resources to "produce" outcomes. By combining structured and unstructured data from various sources, data analytics might provide a solution for governments that aim to monitor the performance of schools more closely. Data analytics in education should not be the domain of a single discipline. Economists should discuss the possibilities, issues, and normative questions with a multidisciplinary team of pedagogists, philosophers, computer scientists, and sociologists. By bringing together various disciplines, a more comprehensive answer can be formulated to the challenges ahead. This book starts this discussion by highlighting some economic perspectives on the use of data analytics in education. The book begins a rich, multidisciplinary discussion that may make data analytics in education seem as natural as a teacher in front of a classroom.

This book addresses the usefulness of knowledge discovery through data mining. With this aim, contributors from different fields propose concrete problems and applications showing how data mining and discovering embedded knowledge from raw data can be beneficial to social organizations, domestic spheres, and ICT markets. Data mining or knowledge discovery in databases (KDD) has received increasing interest due to its focus on transforming large amounts of data into novel, valid, useful, and structured knowledge by detecting concealed patterns and relationships. The concept of knowledge is broad and speculative and has promoted epistemological debates in western philosophies. The intensified interest in knowledge management and data mining stems from the difficulty in identifying computational models able to approximate human behaviors and abilities in resolving organizational, social, and physical problems. Current ICT interfaces are not yet adequately advanced to support and simulate the abilities of physicians, teachers, assistants or housekeepers in domestic spheres. And unlike in industrial contexts where abilities are routinely applied, the domestic world is continuously changing and unpredictable. There are challenging questions in this field: Can knowledge locked in conventions, rules of conduct, common sense, ethics, emotions, laws, cultures, and experiences be mined from data? Is it acceptable for automatic systems displaying emotional behaviors to govern complex interactions based solely on the mining of large volumes of data? Discussing multidisciplinary themes, the book proposes computational models able to approximate, to a certain degree, human behaviors and abilities in resolving organizational, social, and physical problems. The innovations presented are of primary importance for: a. The academic research community b. The ICT market c. Ph.D. students and early stage researchers d. Schools, hospitals, rehabilitation and assisted-living centers e. Representatives from multimedia industries and standardization bodies

This book presents a collection of essays written by leading researchers to honor Roman Slowinski's major scholarly interests and contributions. He is well-known for conducting extensive research on methodologies and techniques for intelligent decision support, where he combines operational research and artificial intelligence. The book reconstructs his main contributions, presents cutting-edge research and provides an outlook on the most promising and advanced domains of computer science and multiple criteria decision aiding. The respective chapters cover a wide range of related research areas, including decision sciences, ordinal data mining, preference learning and multiple criteria decision aiding, modeling of uncertainty and imprecision in decision problems, rough set theory, fuzzy set theory, multi-objective optimization, project scheduling and decision support applications. As such, the book will appeal to researchers and scholars in related fields.

From the Foreword: "Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies." ---Sartaj Sahni, University of Florida, USA "Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields. --Hai Jin, Huazhong University of Science and Technology, China Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.

"R and Data Mining "introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.

Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.

With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, "R and Data Mining" is a valuable, practical guide to a powerful method of analysis.
Presents an introduction into using R for data mining applications, covering most popular data mining techniquesProvides code examples and data so that readers can easily learn the techniquesFeatures case studies in real-world applicationsto help readers apply the techniques in their work"

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.

This book discusses the role of mobile network data in urban informatics, particularly how mobile network data is utilized in the mobility context, where approaches, models, and systems are developed for understanding travel behavior. The objectives of this book are thus to evaluate the extent to which mobile network data reflects travel behavior and to develop guidelines on how to best use such data to understand and model travel behavior. To achieve these objectives, the book attempts to evaluate the strengths and weaknesses of this data source for urban informatics and its applicability to the development and implementation of travel behavior models through a series of the authors' research studies. Traditionally, survey-based information is used as an input for travel demand models that predict future travel behavior and transportation needs. A survey-based approach is however costly and time-consuming, and hence its information can be dated and limited to a particular region. Mobile network data thus emerges as a promising alternative data source that is massive in both cross-sectional and longitudinal perspectives, and one that provides both broader geographic coverage of travelers and longer-term travel behavior observation. The two most common types of travel demand model that have played an essential role in managing and planning for transportation systems are four-step models and activity-based models. The book's chapters are structured on the basis of these travel demand models in order to provide researchers and practitioners with an understanding of urban informatics and the important role that mobile network data plays in advancing the state of the art from the perspectives of travel behavior research.

This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms - the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.

This open access book brings together a set of original studies that use cutting-edge computational methods to investigate conflict at various geographic scales and degrees of intensity and violence. Methodologically, this book covers a variety of computational approaches from text mining and machine learning to agent-based modelling and social network analysis. Empirical cases range from migration policy framing in North America and street protests in Iran to violence against civilians in Congo and food riots world-wide. Supplementary materials in the book include a comprehensive list of the datasets on conflict and dissent, as well as resources to online repositories where the annotated code and data of individual chapters can be found and where (agent-based) models can be re-produced and altered. These materials are a valuable resource for those wishing to retrace and learn from the analyses described in this volume and adapt and apply them to their own research interests. By bringing together novel research through an international team of scholars from a range of disciplines, Computational Conflict Research pioneers and maps this emerging field. The book will appeal to students, scholars, and anyone interested in the prospects of using computational social sciences to advance our understanding of conflict dynamics.

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process DESCRIPTION Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. about the technology Time series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fields-from tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts. about the book Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.

This book provides readers with an insight into the development of a novel method for regridding gridded spatial data, an operation required to perform the map overlay operation and apply map algebra when processing spatial data. It introduces the necessary concepts from spatial data processing and fuzzy rulebase systems and describes the issues experienced when using current regridding algorithms. The main focus of the book is on describing the different modifications needed to make the problem compatible with fuzzy rulebases. It offers a number of examples of out-of-the box thinking to handle aspects such as rulebase construction, defuzzification, spatial data comparison, etc. At first, the emphasis is put on the newly developed method, and additional datasets containing information on the underlying spatial distribution of the data are identified. After this, an artificial intelligent system (in the form of a fuzzy inference system) is constructed using this knowledge and then applied on the input data to perform the regridding. The book offers an example of how an apparently simple problem can pose many different challenges, even when trying to solve it with existing soft computing technologies. The workflow and solutions to solve these challenges are universal and may therefore be broadly applied into other contexts.

The Elements of Knowledge Organization is a unique and original work introducing the fundamental concepts related to the field of Knowledge Organization (KO). There is no other book like it currently available. The author begins the book with a comprehensive discussion of "knowledge" and its associated theories. He then presents a thorough discussion of the philosophical underpinnings of knowledge organization. The author walks the reader through the Knowledge Organization domain expanding the core topics of ontologies, taxonomies, classification, metadata, thesauri and domain analysis. The author also presents the compelling challenges associated with the organization of knowledge. This is the first book focused on the concepts and theories associated with KO domain. Prior to this book, individuals wishing to study Knowledge Organization in its broadest sense would generally collocate their own resources, navigating the various methods and models and perhaps inadvertently excluding relevant materials. This text cohesively links key and related KO material and provides a deeper understanding of the domain in its broadest sense and with enough detail to truly investigate its many facets. This book will be useful to both graduate and undergraduate students in the computer science and information science domains both as a text and as a reference book. It will also be valuable to researchers and practitioners in the industry who are working on website development, database administration, data mining, data warehousing and data for search engines. The book is also beneficial to anyone interested in the concepts and theories associated with the organization of knowledge. Dr. Richard P. Smiraglia is a world-renowned author who is well published in the Knowledge Organization domain. Dr. Smiraglia is editor-in-chief of the journal Knowledge Organization, published by Ergon-Verlag of Wurzburg. He is a professor and member of the Information Organization Research Group at the School of Information Studies at University of Wisconsin Milwaukee.

Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications is a comprehensive reference on concepts, algorithms, theories, applications, software, and visualization of data mining, text mining, Web mining and computing/supercomputing. This publication provides a coherent set of related works on the state-of-the-art of the theory and applications of mining, making it a useful resource for researchers, practitioners, professionals and intellectuals in technical and non-technical fields.