The modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. A huge open-source project, R has become enormously popular because of its power and flexibility. With R you can organise, analyse and visualise data. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science. Learning about data is important in many academic and business settings, and R offers a potent and adaptable programming toolbox. The book covers a range of topics, including: importing/exporting data, summarising data, visualising data, managing and manipulating data objects, data analysis (regression, ANOVA and association among others) and programming functions. Regardless of your background or specialty, you'll find this book the perfect primer on data analysis, data visualisation and data management, and a springboard for further exploration.

Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgrounds for the reader, including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context- sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced. A grammar-based genetic programming system called LOGENPRO (The LOGic grammar based GENetic PROgramming system) is detailed and tested on many problems in data mining. It is found that LOGENPRO outperforms some ILP systems. We have also illustrated how to apply LOGENPRO to emulate Automatically Defined Functions (ADFs) to discover problem representation primitives automatically. By employing various knowledge about the problem being solved, LOGENPRO can find a solution much faster than ADFs and the computation required by LOGENPRO is much smaller than that of ADFs. Moreover, LOGENPRO can emulate the effects of Strongly Type Genetic Programming and ADFs simultaneously and effortlessly. Data Mining Using Grammar Based Genetic Programming and Applications is appropriate for researchers, practitioners and clinicians interested in genetic programming, data mining, and the extraction of data from databases.

Biometric Solutions for Authentication in an E-World provides a collection of sixteen chapters containing tutorial articles and new material in a unified manner. This includes the basic concepts, theories, and characteristic features of integrating/formulating different facets of biometric solutions for authentication, with recent developments and significant applications in an E-world. This book provides the reader with a basic concept of biometrics, an in-depth discussion exploring biometric technologies in various applications in an E-world. It also includes a detailed description of typical biometric-based security systems and up-to-date coverage of how these issues are developed. Experts from all over the world demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms, architectures, and implementations for biometric-based applications in an E-world.

Method Engineering focuses on the design, construction and evaluation of methods, techniques and support tools for information systems development It addresses a number of important topics, including: method representation formalisms; meta-modelling; situational methods; contingency approaches; system development practices of method engineering; terminology and reference models; ontologies; usability and experience reports; and organisational support and impact.

This book introduces quantitative intertextuality, a new approach to the algorithmic study of information reuse in text, sound and images. Employing a variety of tools from machine learning, natural language processing, and computer vision, readers will learn to trace patterns of reuse across diverse sources for scholarly work and practical applications. The respective chapters share highly novel methodological insights in order to guide the reader through the basics of intertextuality. In Part 1, "Theory", the theoretical aspects of intertextuality are introduced, leading to a discussion of how they can be embodied by quantitative methods. In Part 2, "Practice", specific quantitative methods are described to establish a set of automated procedures for the practice of quantitative intertextuality. Each chapter in Part 2 begins with a general introduction to a major concept (e.g., lexical matching, sound matching, semantic matching), followed by a case study (e.g., detecting allusions to a popular television show in tweets, quantifying sound reuse in Romantic poetry, identifying influences in fan faction by thematic matching), and finally the development of an algorithm that can be used to reveal parallels in the relevant contexts. Because this book is intended as a "gentle" introduction, the emphasis is often on simple yet effective algorithms for a given matching task. A set of exercises is included at the end of each chapter, giving readers the chance to explore more cutting-edge solutions and novel aspects to the material at hand. Additionally, the book's companion website includes software (R and C++ library code) and all of the source data for the examples in the book, as well as supplemental content (slides, high-resolution images, additional results) that may prove helpful for exploring the different facets of quantitative intertextuality that are presented in each chapter. Given its interdisciplinary nature, the book will appeal to a broad audience. From practitioners specializing in forensics to students of cultural studies, readers with diverse backgrounds (e.g., in the social sciences, natural language processing, or computer vision) will find valuable insights.

Multimedia data comprising of images, audio and video is becoming increasingly common. The decreasing costs of consumer electronic devices such as digital cameras and digital camcorders, along with the ease of transportation facilitated by the Internet, has lead to a phenomenal rise in the amount of multimedia data generated and distributed. Given that this trend of increased use of multimedia data is likely to accelerate, there is an urgent need for providing a clear means of capturing, storing, indexing, retrieving, analyzing and summarizing such data. Content-based access to multimedia data is of primary importance since it is the natural way by which human beings interact with such information. To facilitate the content-based access of multimedia information, the first step is to derive feature measures from these data so that a feature space representation of the data content can be formed. This can subsequently allow for mapping the feature space to the symbol space (semantics) either automatically or through human intervention. Thus, signal to symbol mapping, useful for any practical system, can be successfully achieved. Perspectives on Content-Based Multimedia Systems provides a comprehensive set of techniques to tackle these important issues. This book offers detailed solutions to a wide range of practical problems in building real systems by providing specifics of three systems built by the authors. While providing a systems focus, it also equips the reader with a keen understanding of the fundamental issues, including a formalism for content-based multimedia database systems, multimedia feature extraction, object-based techniques, signature-based techniques and fuzzy retrieval techniques. The performance evaluation issues of practical systems is also explained. This book brings together essential elements of building a content-based multimedia database system in a way that makes them accessible to practitioners in computer science and electrical engineering. It can also serve as a textbook for graduate-level courses.

"JDBC Metadata, MySQL, and Oracle Recipes" is the only book that focuses on metadata or annotation-based code recipes for JDBC API for use with Oracle and MySQL. It continues where the authors other book, "JDBC Recipes: A Problem-Solution Approach," leaves off.

This edition is also a Java EE 5-compliant book, perfect for lightweight Java database development. And it provides cut-and-paste code templates that can be immediately customized and applied in each developer's application development.

Research Directions in Data and Applications Security describes original research results and innovative practical developments, all focused on maintaining security and privacy in database systems and applications that pervade cyberspace. The areas of coverage include:

-Role-Based Access Control;
-Database Security;
-XML Security;
-Data Mining and Inference;
-Multimedia System Security;
-Network Security;
-Public Key Infrastructure;
-Formal Methods and Protocols;
-Security and Privacy.

Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results.

This book introduces advanced semantic web technologies, illustrating their utility and highlighting their implementation in biological, medical, and clinical scenarios. It covers topics ranging from database, ontology, and visualization to semantic web services and workflows. The volume also details the factors impacting on the establishment of the semantic web in life science and the legal challenges that will impact on its proliferation.

Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered. This book is divided into three parts-- * Data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns * A C++ Data Clustering Framework: The development of data clustering base classes * Data Clustering Algorithms: The implementation of several popular data clustering algorithms A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the CD-ROM of the book. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.

Database and Mobile Computing brings together in one place important contributions and up-to-date research results in this important area. Databases and Mobile Computing serves as an excellent reference, providing insight into some of the most important research issues in the field.

Spatial trajectories have been bringing the unprecedented wealth to a variety of research communities. A spatial trajectory records the paths of a variety of moving objects, such as people who log their travel routes with GPS trajectories. The field of moving objects related research has become extremely active within the last few years, especially with all major database and data mining conferences and journals.

"Computing with Spatial Trajectories" introduces the algorithms, technologies, and systems used to process, manage and understand existing spatial trajectories for different applications. This book also presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks. Each chapter provides readers with a tutorial-style introduction to one important aspect of location trajectory computing, case studies and many valuable references to other relevant research work.

"Computing with Spatial Trajectories" is designed as a reference or secondary text book for advanced-level students and researchers mainly focused on computer science and geography. Professionals working on spatial trajectory computing will also find this book very useful.

Knowledge-based (KB) technology is being applied to complex problem-solving and critical tasks in many application domains. Concerns have naturally arisen as to the dependability of knowledge-based systems (KBS). As with any software, attention to quality and safety must be paid throughout development of a KBS and rigorous verification and validation (V&V) techniques must be employed. Research in V&V of KBS has emerged as a distinct field only in the last decade and is intended to address issues associated with quality and safety aspects of KBS and to credit such applications with the same degree of dependability as conventional applications. In recent years, V&V of KBS has been the topic of annual workshops associated with the main AI conferences, such as AAAI, IJACI and ECAI. Validation and Verification of Knowledge Based Systems contains a collection of papers, dealing with all aspects of KBS V&V, presented at the Fifth European Symposium on Verification and Validation of Knowledge Based Systems and Components (EUROVAV'99 - http: //www.dnv.no/research/safekbs/eurovav99/) which was held in Oslo in the summer of 1999, and was sponsored by Det Norske Veritas and the British Computer Society's Specialist Group on Expert Systems (SGES).

High Performance Data Mining: Scaling Algorithms, Applications and Systems brings together in one place important contributions and up-to-date research results in this fast moving area. High Performance Data Mining: Scaling Algorithms, Applications and Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

The book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way. At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book. Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout.

This is a compilation of papers presented at the Information System Concepts conference in Marburg, Germany. The special focus is consolidation and harmonisation of the numerous and widely diverging views in the field of information systems. This issue has become a hot topic, as many leading information system researchers and practitioners come to realise the importance of better communication among the members of the information systems community, and of a better scientific foundation of this rapidly evolving field.

Continuous improvements in data analysis and cloud computing have allowed more opportunities to develop systems with user-focused designs. This not only leads to higher success in day-to-day usage, but it increases the overall probability of technology adoption. Advancing Cloud Database Systems and Capacity Planning with Dynamic Applications is a key resource on the latest innovations in cloud database systems and their impact on the daily lives of people in modern society. Highlighting multidisciplinary studies on information storage and retrieval, big data architectures, and artificial intelligence, this publication is an ideal reference source for academicians, researchers, scientists, advanced level students, technology developers and IT officials.

Current database technology and computer hardware allow us to gather, store, access, and manipulate massive volumes of raw data in an efficient and inexpensive manner. In addition, the amount of data collected and warehoused in all industries is growing every year at a phenomenal rate. Nevertheless, our ability to discover critical, non-obvious nuggets of useful information in data that could influence or help in the decision making process, is still limited. Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field that focuses on the overall process of information discovery from large volumes of data. The field combines database concepts and theory, machine learning, pattern recognition, statistics, artificial intelligence, uncertainty management, and high-performance computing. To remain competitive, businesses must apply data mining techniques such as classification, prediction, and clustering using tools such as neural networks, fuzzy logic, and decision trees to facilitate making strategic decisions on a daily basis. Knowledge Discovery for Business Information Systems contains a collection of 16 high quality articles written by experts in the KDD and DM field from the following countries: Austria, Australia, Bulgaria, Canada, China (Hong Kong), Estonia, Denmark, Germany, Italy, Poland, Singapore and USA.

Data mining is the process of extracting hidden patterns from data, and it's commonly used in business, bioinformatics, counter-terrorism, and, increasingly, in professional sports. First popularized in Michael Lewis' best-selling Moneyball: The Art of Winning An Unfair Game, it is has become an intrinsic part of all professional sports the world over, from baseball to cricket to soccer. While an industry has developed based on statistical analysis services for any given sport, or even for betting behavior analysis on these sports, no research-level book has considered the subject in any detail until now. Sports Data Mining brings together in one place the state of the art as it concerns an international array of sports: baseball, football, basketball, soccer, greyhound racing are all covered, and the authors (including Hsinchun Chen, one of the most esteemed and well-known experts in data mining in the world) present the latest research, developments, software available, and applications for each sport. They even examine the hidden patterns in gaming and wagering, along with the most common systems for wager analysis.

This book outlines the consequences of digitization for peer-reviewed research articles published in electronic journals. It has often been argued that digitization will revolutionize scientific communication. However, this study shows that this is not the case as far as scientific journals are concerned. Authors make little or no use of the possibilities offered by the digital medium, new procedures for electronic peer review have not replaced traditional peer review, and users do not seem to accept new forms of interaction offered by some electronic journals. The main innovations are to be found at the level of the infrastructures developed by publishers. Scientists themselves appear to be reluctant to change their established patterns of behaviour in formal scientific communication.

The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.

Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making. Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.

The book reports on the latest advances and challenges of soft computing. Itgathers original scientific contributions written by top scientists in the fieldand 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."

"Change Management for Semantic Web Services" provides a thorough analysis of change management in the lifecycle of services for databases and workflows, including changes that occur at the individual service level or at the aggregate composed service level. This book describes taxonomy of changes that are expected in semantic service oriented environments. The process of change management consists of detecting, propagating, and reacting to changes.

"Change Management for Semantic Web Services" is one of the first books that discuss the development of a theoretical foundation for managing changes in atomic and long-term composed services. This book also proposes a formal model and a change language to provide sufficient semantics for change management; it devises an automatic process to react to, verify, and optimize changes. Case studies and examples are presented in the last section of this book.