Multimedia Cartography provides a contemporary overview of the issues related to multimedia cartography and the design and production elements that are unique to this area of mapping. The book has been written for professional cartographers interested in moving into multimedia mapping, for cartographers already involved in producing multimedia titles who wish to discover the approaches that other practitioners in multimedia cartography have taken and for students and academics in the mapping sciences and related geographical fields wishing to update their knowledge about current issues related to cartographic design and production. It provides a new approach to cartography one based on the exploitation of the many rich media components and avant-garde approach that multimedia offers."

Die Herausgeber und Autoren fuhren praxisnah in neue Methoden und Technologien des (Meta-)Daten-Managements fur die Vernetzung und Integration verteilter, heterogener Datenbestande ein. Dabei werden die neuen Technologien und Methoden von bereits etablierten Ansatzen deutlich abgegrenzt, ihre Potenziale und auch ihre Grenzen klar benannt. Vor allem Semantic-Web-Technologien, deren betrieblicher Einsatz anhand anschaulicher Fallstudien erlautert wird, spielen eine zentrale Rolle."

Data has dramatically changed how our world works. Understanding and using data is now one of the most transferable and desirable skills out there - whether you're an entrepreneur wanting to boost your business, a job-seeker looking for that employable edge, or hoping to make the most of your current career. Learning how to work with data may seem intimidating or difficult - but don't worry, Confident Data Skills is here to help. This updated second edition takes you through the basics of data: from data mining and preparing and analyzing your data, to visualizing and communicating your insights, and now with exciting new content on neural networks and deep learning. Featuring in-depth international case studies from companies like Netflix, LinkedIn and Mike's Hard Lemonade Co., as well as easy-to understand language and inspiring advice and guidance, Confident Data Skills help you use your new-found data skills to give your career that cutting-edge boost. About the Confident series... From coding and web design to data, digital content and cyber security, the Confident books are the perfect beginner's resource for enhancing your professional life, whatever your career path..

It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690) Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, "analytics," is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology? This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.

Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can't scale data science teams fast enough to keep up with the growing amounts of data to transform. What's the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization

Active Enterprise Intelligence ist der ganzheitliche Ansatz einer Informationslogistik von Teradata, der zwischen Strategic und Operational Intelligence unterscheidet, diese aber in einer integrierten Betrachtungsweise auf Basis eines unternehmensweiten Active Data Warehouses wieder zusammenfuhrt. Dieses Buch verbindet erstmals den Teradata-Ansatz mit der St. Galler Schule der Unternehmensweiten Informationslogistik. Aktuelle Herausforderungen und Losungsansatze der Informationslogistik werden thematisiert und Hinweise zu ihrer Ausgestaltung gegeben.

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data world that we inhabit. Statistical Learning and Data Science is a work of reference in the rapidly evolving context of converging methodologies. It gathers contributions from some of the foundational thinkers in the different fields of data analysis to the major theoretical results in the domain. On the methodological front, the volume includes conformal prediction and frameworks for assessing confidence in outputs, together with attendant risk. It illustrates a wide range of applications, including semantics, credit risk, energy production, genomics, and ecology. The book also addresses issues of origin and evolutions in the unsupervised data analysis arena, and presents some approaches for time series, symbolic data, and functional data. Over the history of multidimensional data analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and unsupervised data analysis, provide great capability for addressing the digital data deluge. Exploring the foundations and recent breakthroughs in the field, Statistical Learning and Data Science demonstrates how data analysis can improve personal and collective health and the well-being of our social, business, and physical environments.

This book constitutes the refereed proceedings of the Third International Conference on Advanced Data Mining and Applications, ADMA 2007, held in Harbin, China in August 2007.

The 44 revised full papers and 15 revised short papers presented together with the abstract of 1 invited lecture were carefully reviewed and selected from about 200 submissions. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining. The major theme of the conference encompasses the innovative applications of data mining approaches to real-world problems that involve large data sets, incomplete and noisy data, or demand optimal solutions.

This book constitutes the refereed proceedings of the 7th Industrial Conference on Data Mining, ICDM 2007, held in Leipzig, Germany in July 2007.

The 25 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 96 submissions. The papers are organized in topical sections on aspects of classification and prediction, clustering, web mining, data mining in medicine, applications of data mining, time series and frequent pattern mining, and association rule mining.

This book constitutes the refereed proceedings of the 7th International Conference on Intelligent Data Analysis, IDA 2007, held in Ljubljana, Slovenia, September 6-8, 2007.

The 33 revised papers presented were carefully reviewed and selected from almost 100 submissions. All current aspects of this interdisciplinary field are addressed; the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.

This volume provides a snapshot of the current state of the art in data mining, presenting it both in terms of technical developments and industrial applications. The collection of chapters is based on works presented at the Australasian Data Mining conferences and industrial forums. Authors include some of Australia's leading researchers and practitioners in data mining. The volume also contains chapters by regional and international authors.

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.

Military organizations around the world are normally huge producers and consumers of data. Accordingly, they stand to gain from the many benefits associated with data analytics. However, for leaders in defense organizations-either government or industry-accessible use cases are not always available. This book presents a diverse collection of cases that explore the realm of possibilities in military data analytics. These use cases explore such topics as: Context for maritime situation awareness Data analytics for electric power and energy applications Environmental data analytics in military operations Data analytics and training effectiveness evaluation Harnessing single board computers for military data analytics Analytics for military training in virtual reality environments A chapter on using single board computers explores their application in a variety of domains, including wireless sensor networks, unmanned vehicles, and cluster computing. The investigation into a process for extracting and codifying expert knowledge provides a practical and useful model for soldiers that can support diagnostics, decision making, analysis of alternatives, and myriad other analytical processes. Data analytics is seen as having a role in military learning, and a chapter in the book describes the ongoing work with the United States Army Research Laboratory to apply data analytics techniques to the design of courses, evaluation of individual and group performances, and the ability to tailor the learning experience to achieve optimal learning outcomes in a minimum amount of time. Another chapter discusses how virtual reality and analytics are transforming training of military personnel. Virtual reality and analytics are also transforming monitoring, decision making, readiness, and operations. Military Applications of Data Analytics brings together a collection of technical and application-oriented use cases. It enables decision makers and technologists to make connections between data analytics and such fields as virtual reality and cognitive science that are driving military organizations around the world forward.

Data Entry and Validation with C# and VB .NET Windows Forms is a complete text on how to write effective data entry and validation code. Most books deal only with the individual pieces of .NET, such as the controls or how the .NET Framework works. This book brings together all this knowledge and shows readers how to build real programs. The old hacker adage Garbage in, garbage out has never been so important as it is today. With ever-increasing amounts of information flowing into and out of modern applications, the task of an application developer to control and verify information is critically important to any software project. For the first time, Data Entry and Validation with C# and VB .NET Windows Forms brings together current knowledge on this subject in an understandable, easy-to-read form. Covering development and best practices for data entry and validation, including GDI+, custom controls, localization, accessibility, proper data validation techniques, and best practices with Visual Basic and C#, Data Entry and Validation with C# and VB .NET Windows Forms is a book no modern programmer should be without.

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

Facing rapidly growing challenges in empirical research, this volume presents a selection of new methods and approaches in the field of Exploratory Data Analysis. The interested reader will find numerous ideas and examples for cross disciplinary applications of classification and data analysis methods in fields such as data and web mining, medicine and biological sciences as well as marketing, finance and management sciences.

In this monograph, we study the problem of high-dimensional indexing and systematically introduce two efficient index structures: one for range queries and the other for similarity queries. Extensive experiments and comparison studies are conducted to demonstrate the superiority of the proposed indexing methods.Many new database applications, such as multimedia databases or stock price information systems, transform important features or properties of data objects into high-dimensional points. Searching for objects based on these features is thus a search of points in this feature space. To support efficient retrieval in such high-dimensional databases, indexes are required to prune the search space. Indexes for low-dimensional databases are well studied, whereas most of these application specific indexes are not scaleable with the number of dimensions, and they are not designed to support similarity searches and high-dimensional joins.

The volume presents new developments in data analysis and classification and gives an overview of the state of the art in these scientific fields and relevant applications. Areas that receive considerable attention in the book are clustering, discrimination, data analysis, and statistics, as well as applications in economics, biology, and medicine. The reader will find material on recent technical and methodological developments and a large number of application papers demonstrating the usefulness of the newly developed techniques.

This book constitutes the refereed proceedings of an international workshop on Pattern Detection and Discovery organized by the European Science Foundation in London, UK in September 2002.The 17 revised full papers presented were carefully selected and reviewed for inclusion in this state-of-the-art book. Six papers present an introduction and general issues in the emerging field. Four papers are devoted to association rules. Four papers deal with various aspects of text mining and Web mining, and three papers explore advanced applications.

This book constitutes the refereed proceedings of the First International Conference on Graph Transformations, ICGT 2002, held in Barcelona, Spain in October 2002.The 26 revised full papers presented were carefully reviewed and selected by the program committe. Also included are abstracts of 3 invited papers, a tutorial, the extended abstract of a tutorial, and 5 reports of workshops held in conjunction with ICGT. The papers deal with various graphical structures that are useful to describe complex systems and computational structures, like graphs, diagrams, visual sentences, and others. Graph transformations are stongly related to graph theory, graph algorithms, formal language and parsing theory, the theory of concurrent and distributed systems, formal specification and verification, and logic and semantics.

This book constitutes the refereed proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2002, held in Helsinki, Finland in August 2002.The 39 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are kernel methods, probabilistic methods, association rule mining, rough sets, sampling algorithms, pattern discovery, web text mining, meta data clustering, rule induction, information extraction, dependency detection, rare class prediction, classifier systems, text classification, temporal sequence analysis, unsupervised learning, time series analysis, medical data mining, etc.

This book constitutes the refereed proceedings of the 5th International Workshop on Document Analysis Systems, DAS 2002, held in Princeton, NJ, USA in August 2002 with sponsorship from IAPR.The 44 revised full papers presented together with 14 short papers were carefuly reviwed and selected for inclusion in the book. All current issues in document analysis systems are adressed. The papers are organized in topical sections on OCR features and systems, handwriting recognition, layout analysis, classifiers and learning, tables and forms, text extraction, indexing and retrieval, document engineering, and new applications.

This book deals with recent developments in classification and data analysis and presents new topics which are of central interest to modern statistics. In particular, these include: classification models and clustering methods, multivariate data analysis, symbolic data, neural networks and learning devices, phylogeny and bioinformatics, new software systems for classification and data analysis, as well as applications in social, economic, biological, medical and other sciences. The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention.The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.

This book constitutes the refereed proceedings of the 10th International Conference on Digital Geometry for Computer Imagery, DGCI 2002, held in Bordeaux, France, in April 2002.The 22 revised full papers and 13 posters presented together with 3 invited papers were carefully reviewed and selected from 67 submissions. The papers are organized in topical sections on topology, combinatorial image analysis, morphological analysis, shape representation, models for discrete geometry, segmentation and shape recognition, and applications.