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this article, we focus on some of the practical issues concerning visualizing the dynamics of specialties in a scientific discipline. What are the key characteristics of scientific revolutions that should be featured in visualization models? To what extent do citation patterns track scientific revolutions? What are the growth patterns of scientific paradigms in terms of their citation impact and connectivity? In addition, we visualize the growth patterns of superstring revolutions in physics so as to illustrate the feasibility and viability of our approach

We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent-child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user study indicates that CiteWiz is significantly more efficient than traditional database interfaces for high-level analysis tasks relating to influence and overviews, and equally efficient for low-level tasks such as finding a paper and correlating bibliographical data.

The importance of information as a resource for economic growth and education is steadily increasing. Due to technological advances in computer industry and the explosive growth of the Internet much valuable information will be available in digital libraries. This paper introduces a system that aims to support a user's browsing activities in document sets retrieved from a digital library. Latent Semantic Analysis is applied to extract salient semantic structures and citation patterns of documents stored in a digital library in a computationally expensive batch job. At retrieval time, cluster techniques are used to organize retrieved documents into clusters according to the previously extracted semantic similarities. A modified Boltzman algorithm [1] is employed to spatially organize the resulting clusters and their documents in the form of a three-dimensional information landscape or "i-scape". The i-scape is then displayed for interactive exploration via a multi-modal, virtual reality CAVE interface [8]. Users' browsing activities are recorded and user models are extracted to give newcomers online help based on previous navigation activity as well as to enable experienced users to recognize and exploit past user traces.

Author co-citation analysis (ACA) has been widely used in bibliometrics as an analytical method in analyzing the intellectual structure of science studies. It can be used to identify authors fromthe same or similar research fields. However, such analysis method relies heavily on statistical tools to perform the analysis and requires human interpretation. Web Citation Database is a data warehouse used for storing citation indices of Web publications. In this paper, we propose a mining process to automate the ACA based on the Web Citation Database. The mining process uses agglomerative hierarchical clustering (AHC) as the mining technique for author clustering and multidimensional scaling (MDS) for displaying author cluster maps. The clustering results and author cluster map have been incorporated into a citation-based

Introduction TheInternetandWWWhavealreadyestablishedthemselvesasmajorfactorsintheoperationofscholarlycom - munitiesworldwide.Today,theInternetisusedinall spheresoflifeforexchangeofinformation.Information resourcesontheInternetareincreasingtremendously.GordonandPathak (1999)suggestedthattheprimaryuseofthe Internetisforinformationretrieval.Searchenginesare consideredasthemostimportanttoolforretrievinginformationontheWeb, andconsequentlyformacriticalareaof research(Gaines,Chen,&Shaw,1997;Lawrence&Giles, 1998). DespitetheeffectivenessofInternet-basedoronlineinformationretrieval, problemsstillexist.Woodward(1996) arguedthattheInternetiscurrentlyinastateofnearchaos intermsofaccessandorganizationofinformation.Voorbij (1999)foundthat67%oftheInternetusersagreeor stronglyagreewiththedifficultytoperformsubject searchesontheInternet.Users,especiallythenoviceand irregularusers,finditdifficulttophrasetheirinformation needsduetothelackofknowledgeliteracyinsearch domain(Bates,1986,1998).Alth

As more and more information is available on the Internet, search engines and bookmark tools become very popular. However, most search tools are based on character-level matching without any semantic analysis, and users have to manually organize their bookmarks or favorite collections without any convenient tool to help them identify the subjects of the Web pages. In this paper, we introduce an interactive tool that automatically analyzes, categorizes, and visualizes the semantic relationships of web pages in personal bookmark or favorites collections based on their semantic similarity. Sophisticated data analysis methods are applied to retrieve and analyze the full text of the Web pages. The Web pages are clustered hierarchically based on their semantic similarities . A utility measure is recursively applied to determine the best partitions that are visualized by what we call the Semantic Treemap. Various interaction methods such as scrolling, zooming, expanding, selecting, searching, filtering etc. are provided to facilitate viewing and querying for information. Furthermore, the hierarchical organization as well as the semantic similarities among Web pages can be exported and visualized in a collaborative 3D environment, allowing a group of people to compare and share each other's bookmarks.

Digital images and videos have an increasingly important role in today's telecommunication and our everyday life in modern information society. The past few years witnessed a proliferation of content-based image retrieval techniques. Images are typically characterized by intrinsic attributes of images such as color, texture, and shape. However, the potential of integrating these techniques with visualization and data-mining techniques has yet been fully explored. Users should be able to explore images in a database or video clips by visual similarities. In this article, we explore the synergy between Pathfinder networks and content-based information retrieval techniques. Salient structures of images are revealed through visualization models derived from features extracted from images. Visualizations are generated from three feature classes of the well-known QBIC system: color, layout, and texture. 1. INTRODUCTION Digital images and videos have an increasingly important role in today'...

Aggregates of links are of interest to information scientists in the same way as citation counts are: as potential sources of data from which new knowledge can be mined. The recent discovery of a correlation between a web link count measure and the research quality of British universities is built upon by applying a range of multivariate statistical techniques to counts of links between pairs of universities. This represents an initial attempt at developing an understanding of this phenomenon. Plausible results are extracted, including the high degree of similarity between the Scottish universities and limited evidence of a dichotomy between new and traditional universities. Outliers in the data were also identified by the techniques, some of which were verified by being tracked down to identifiable web phenomena. This is an important outcome because successful anomaly identification is a precondition to more effective analysis of this kind of data. The identification of groupings is encouraging evidence that web links between universities can be mined for significant results, although it is clear that more methodological development is needed if any but the simplest patterns are to be extracted. Finally, based upon the types of patterns extracted it is argued that none of the methods used are capable of fully analysing link structures on their own.

We present the application of a new knowledge visualization tool, VxInsight, to the mapping and analysis of patent databases. Patent data are mined and placed in a database, relationships between the patents are identified, primarily using the citation and classification structures, then the patents are clustered using a proprietary force-directed placement algorithm. Related patents cluster together to produce a 3-D landscape view of the tens of thousands of patents. The user can navigate the landscape by zooming into or out of regions of interest. Querying the underlying database places a colored marker on each patent matching the query. Automatically generated labels, showing landscape content, update continually upon zooming. Optionally, citation links between patents may be shown on the landscape. The combination of these features enables powerful analyses of patent databases.