Search (48 results, page 2 of 3)

  • × theme_ss:"Visualisierung"
  1. Cao, N.; Sun, J.; Lin, Y.-R.; Gotz, D.; Liu, S.; Qu, H.: FacetAtlas : Multifaceted visualization for rich text corpora (2010) 0.01 
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  2. Yan, B.; Luo, J.: Filtering patent maps for visualization of diversification paths of inventors and organizations (2017) 0.01 
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  3. Zhang, Y.; Zhang, G.; Zhu, D.; Lu, J.: Scientific evolutionary pathways : identifying and visualizing relationships for scientific topics (2017) 0.01 
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  4. Zou, J.; Thoma, G.; Antani, S.: Unified deep neural network for segmentation and labeling of multipanel biomedical figures (2020) 0.01 
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  5. Munzner, T.: Interactive visualization of large graphs and networks (2000) 0.01 
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    Abstract
    Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable.
  6. Hajdu Barat, A.: Human perception and knowledge organization : visual imagery (2007) 0.01 
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    Footnote
    Beitrag in einem Themenheft "3D visualisation "
  7. Collins, L.M.; Hussell, J.A.T.; Hettinga, R.K.; Powell, J.E.; Mane, K.K.; Martinez, M.L.B.: Information visualization and large-scale repositories (2007) 0.01 
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    Footnote
    Beitrag in einem Themenheft "3D visualisation "
  8. Huang, S.-C.; Bias, R.G.; Schnyer, D.: How are icons processed by the brain? : Neuroimaging measures of four types of visual stimuli used in information systems (2015) 0.01 
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  9. Information visualization in data mining and knowledge discovery (2002) 0.01 
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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.
  10. Quirin, A.; Cordón, O.; Santamaría, J.; Vargas-Quesada, B.; Moya-Anegón, F.: ¬A new variant of the Pathfinder algorithm to generate large visual science maps in cubic time (2008) 0.01 
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  11. Singh, A.; Sinha, U.; Sharma, D.k.: Semantic Web and data visualization (2020) 0.01 
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    Source
    Data visualization and knowledge engineering. Eds. J. Hemanth, et al
  12. Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.01 
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  13. Catarci, T.; Spaccapietra, S.: Visual information querying (2002) 0.00 
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    Abstract
    The two facets of "going visual" are usually referred to as visual query systems, for query formulation, and information visualization, for result display. Visual Query Systems (VQSs) are defined as systems for querying databases that use a visual representation to depict the domain of interest and express related requests. VQSs provide both a language to express the queries in a visual format and a variety of functionalities to facilitate user-system interaction. As such, they are oriented toward a wide spectrum of users, especially novices who have limited computer expertise and generally ignore the inner structure of the accessed database. Information visualization, an increasingly important subdiscipline within the field of Human-Computer Interaction (HCI), focuses an visual mechanisms designed to communicate clearly to the user the structure of information and improve an the cost of accessing large data repositories. In printed form, information visualization has included the display of numerical data (e.g., bar charts, plot charts, pie charts), combinatorial relations (e.g., drawings of graphs), and geographic data (e.g., encoded maps). In addition to these "static" displays, computer-based systems, such as the Information Visualizer and Dynamic Queries, have coupled powerful visualization techniques (e.g., 3D, animation) with near real-time interactivity (i.e., the ability of the system to respond quickly to the user's direct manipulation commands). Information visualization is tightly combined with querying capabilities in some recent database-centered approaches. More opportunities for information visualization in a database environment may be found today in data mining and data warehousing applications, which typically access large data repositories. The enormous quantity of information sources an the World-Wide Web (WWW) available to users with diverse capabilities also calls for visualization techniques. In this article, we survey the main features and main proposals for visual query systems and touch upon the visualization of results mainly discussing traditional visualization forms. A discussion of modern database visualization techniques may be found elsewhere. Many related articles by Daniel Keim are available at http://www. informatik.uni-halle.de/dbs/publications.html.
  14. Dushay, N.: Visualizing bibliographic metadata : a virtual (book) spine viewer (2004) 0.00 
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    Abstract
    When our experience of information discovery is mediated by a computer, we neither move ourselves nor the monitor. We have only the computer's monitor to view, and the keyboard and/or mouse to manipulate what is displayed there. Computer interfaces often reduce our ability to get a sense of the contents of a library: we don't perceive the scope of the library: its breadth, (the quantity of materials/information), its density (how full the shelves are, how thorough the collection is for individual topics), or the general audience for the materials (e.g., whether the materials are appropriate for middle school students, college professors, etc.). Additionally, many computer interfaces for information discovery require users to scroll through long lists, to click numerous navigational links and to read a lot of text to find the exact text they want to read. Text features of resources are almost always presented alphabetically, and the number of items in these alphabetical lists sometimes can be very long. Alphabetical ordering is certainly an improvement over no ordering, but it generally has no bearing on features with an inherent non-alphabetical ordering (e.g., dates of historical events), nor does it necessarily group similar items together. Alphabetical ordering of resources is analogous to one of the most familiar complaints about dictionaries: sometimes you need to know how to spell a word in order to look up its correct spelling in the dictionary. Some have used technology to replicate the appearance of physical libraries, presenting rooms of bookcases and shelves of book spines in virtual 3D environments. This approach presents a problem, as few book spines can be displayed legibly on a monitor screen. This article examines the role of book spines, call numbers, and other traditional organizational and information discovery concepts, and integrates this knowledge with information visualization techniques to show how computers and monitors can meet or exceed similar information discovery methods. The goal is to tap the unique potentials of current information visualization approaches in order to improve information discovery, offer new services, and most important of all, improve user satisfaction. We need to capitalize on what computers do well while bearing in mind their limitations. The intent is to design GUIs to optimize utility and provide a positive experience for the user.
  15. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.00 
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  16. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.00 
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  17. Trunk, D.: Semantische Netze in Informationssystemen : Verbesserung der Suche durch Interaktion und Visualisierung (2005) 0.00 
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  18. Palm, F.: QVIZ : Query and context based visualization of time-spatial cultural dynamics (2007) 0.00 
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    Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
  19. Thissen, F.: Screen-Design-Handbuch : Effektiv informieren und kommunizieren mit Multimedia (2001) 0.00 
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  20. Jäger-Dengler-Harles, I.: Informationsvisualisierung und Retrieval im Fokus der Infromationspraxis (2013) 0.00 
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