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Abstract

This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature - an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.

Date

22. 7.2006 16:11:05

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Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
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.
With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."

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Abstract

We propose an approach to visualizing the scientific world and its evolution by constructing minimum spanning trees (MSTs) and a two-dimensional map of scientific journals using the database of the Science Citation Index (SCI) during 1994-2001. The structures of constructed MSTs are consistent with the sorting of SCI categories. The map of science is constructed based on our MST results. Such a map shows the relation among various knowledge clusters and their citation properties. The temporal evolution of the scientific world can also be delineated in the map. In particular, this map clearly shows a linear structure of the scientific world, which contains three major domains including physical sciences, life sciences, and medical sciences. The interaction of various knowledge fields can be clearly seen from this scientific world map. This approach can be applied to various levels of knowledge domains.

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Abstract

This article aims to stimulate research on business process oriented knowledge infrastructures by pointing to the power of visualizations. It claims that business process oriented knowledge infrastructure research is stuck and therefore needs to reinvent and revitalize itself with new impulses. One such stimulus is the use of visualization techniques in business process oriented knowledge infrastructures, with the aim to improve knowledge transfer, knowledge communication, and knowledge creation. First, this article presents an overview on related visualization research. Second, it proposes the Knowledge Visualization Framework as a theoretical backbone where business process oriented knowledge infrastructure research can anchor itself. The framework points to the key questions that need to be answered when visual methods are used in business process oriented knowledge infrastructures. Finally, the article compares the Tube Map Visualization with the Gantt Chart, and proves that the new format excels the traditional approach in regards to various tasks. The findings from the evaluation of 44 interviews indicates that the Project Tube Map is more effective for (1) drawing attention and keeping interest, (2) presenting overview and detail, (3) visualizing who is collaborating with whom, (4) motivating people to participate in the project, and (5) increasing recall. The results presented in this paper are important for researchers and practitioners in the fields of Knowledge Management, Knowledge Visualization, Project Management, and Visual Communication Sciences.

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Abstract

This chapter reviews visualization techniques that can be used to map the ever-growing domain structure of scientific disciplines and to support information retrieval and classification. In contrast to the comprehensive surveys conducted in traditional fashion by Howard White and Katherine McCain (1997, 1998), this survey not only reviews emerging techniques in interactive data analysis and information visualization, but also depicts the bibliographical structure of the field itself. The chapter starts by reviewing the history of knowledge domain visualization. We then present a general process flow for the visualization of knowledge domains and explain commonly used techniques. In order to visualize the domain reviewed by this chapter, we introduce a bibliographic data set of considerable size, which includes articles from the citation analysis, bibliometrics, semantics, and visualization literatures. Using tutorial style, we then apply various algorithms to demonstrate the visualization effectsl produced by different approaches and compare the results. The domain visualizations reveal the relationships within and between the four fields that together constitute the focus of this chapter. We conclude with a general discussion of research possibilities. Painting a "big picture" of scientific knowledge has long been desirable for a variety of reasons. Traditional approaches are brute forcescholars must sort through mountains of literature to perceive the outlines of their field. Obviously, this is time-consuming, difficult to replicate, and entails subjective judgments. The task is enormously complex. Sifting through recently published documents to find those that will later be recognized as important is labor intensive. Traditional approaches struggle to keep up with the pace of information growth. In multidisciplinary fields of study it is especially difficult to maintain an overview of literature dynamics. Painting the big picture of an everevolving scientific discipline is akin to the situation described in the widely known Indian legend about the blind men and the elephant. As the story goes, six blind men were trying to find out what an elephant looked like. They touched different parts of the elephant and quickly jumped to their conclusions. The one touching the body said it must be like a wall; the one touching the tail said it was like a snake; the one touching the legs said it was like a tree trunk, and so forth. But science does not stand still; the steady stream of new scientific literature creates a continuously changing structure. The resulting disappearance, fusion, and emergence of research areas add another twist to the tale-it is as if the elephant is running and dynamically changing its shape. Domain visualization, an emerging field of study, is in a similar situation. Relevant literature is spread across disciplines that have traditionally had few connections. Researchers examining the domain from a particular discipline cannot possibly have an adequate understanding of the whole. As noted by White and McCain (1997), the new generation of information scientists is technically driven in its efforts to visualize scientific disciplines. However, limited progress has been made in terms of connecting pioneers' theories and practices with the potentialities of today's enabling technologies. If the difference between past and present generations lies in the power of available technologies, what they have in common is the ultimate goal-to reveal the development of scientific knowledge.

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Content

Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".

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Date

22. 3.2008 14:29:25

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Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas