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Abstract

Cartographic maps have guided our explorations for centuries, allowing us to navigate the world. Science maps have the potential to guide our search for knowledge in the same way, helping us navigate, understand, and communicate the dynamic and changing structure of science and technology. Allowing us to visualize scientific results, science maps help us make sense of the avalanche of data generated by scientific research today. Atlas of Science, features more than thirty full-page science maps, fifty data charts, a timeline of science-mapping milestones, and 500 color images; it serves as a sumptuous visual index to the evolution of modern science and as an introduction to "the science of science"--charting the trajectory from scientific concept to published results. Atlas of Science, based on the popular exhibit "Places & Spaces: Mapping Science," describes and displays successful mapping techniques. The heart of the book is a visual feast: Claudius Ptolemy's Cosmographia World Map from 1482; a guide to a PhD thesis that resembles a subway map; "the structure of science" as revealed in a map of citation relationships in papers published in 2002; a periodic table; a history flow visualization of the Wikipedia article on abortion; a globe showing the worldwide distribution of patents; a forecast of earthquake risk; hands-on science maps for kids; and many more. Each entry includes the story behind the map and biographies of its makers. Not even the most brilliant minds can keep up with today's deluge of scientific results. Science maps show us the landscape of what we know. Exhibition Ongoing National Science Foundation, Washington, D.C. The Institute for Research Information and Quality Assurance, Bonn, Germany Storm Hall, San Diego State College

Date

22. 1.2017 17:12:16

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Issue

Sponsored by IEEE Computer Society Technical Committee on Visualization and Graphics.

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Abstract

This book is the outcome of the Dagstuhl Seminar on "Information Visualization - Human-Centered Issues in Visual Representation, Interaction, and Evaluation" held at Dagstuhl Castle, Germany, from May 28 to June 1, 2007. Information Visualization (InfoVis) is a relatively new research area, which focuses on the use of visualization techniques to help people understand and analyze data.This book documents and extends the findings and discussions of the various sessions in detail. The seven contributions cover the most important topics: Part I is on general reflections on the value of information visualization; evaluating information visualizations; theoretical foundations of information visualization; teaching information visualization. Part II deals with specific aspects on creation and collaboration: engaging new audiences for information visualization; process and pitfalls in writing information visualization research papers; and visual analytics: definition, process, and challenges.

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Date

23. 3.2008 19:10:22

Footnote

In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.