Search (34 results, page 1 of 2)

  • × theme_ss:"Visualisierung"
  1. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.06
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    Content
    Diplomarbeit, Freie Universität Berlin / Fachbereich Erziehungswissenschaft und Psychologie / Diplomstudiengang Psychologie; vgl.: http://heikohaller.de/literatur/diplomarbeit/ bzw. www.heikohaller.de/toolvergleich/.
    Date
    30. 5.2010 16:22:35
    Imprint
    Berlin : Freie Universität / Fachbereich Erziehungswissenschaft und Psychologie
  2. Chen, C.: CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature (2006) 0.01
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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
  3. Pejtersen, A.M.: ¬The BookHouse : an icon based database system for fiction retrieval in public libraries (1992) 0.01
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    Source
    The marketing of library and information services, 2. Ed.: B. Cronin
  4. Batorowska, H.; Kaminska-Czubala, B.: Information retrieval support : visualisation of the information space of a document (2014) 0.01
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  5. Yukimo Kobashio, N.; Santos, R.N.M.: Information organization and representation by graphic devices : an interdisciplinary approach (2007) 0.01
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    Source
    ¬La interdisciplinariedad y la transdisciplinariedad en la organización del conocimiento científico : actas del VIII Congreso ISKO-España, León, 18, 19 y 20 de Abril de 2007 : Interdisciplinarity and transdisciplinarity in the organization of scientific knowledge. Ed.: B. Rodriguez Bravo u. M.L Alvite Diez
  6. Aris, A.; Shneiderman, B.; Qazvinian, V.; Radev, D.: Visual overviews for discovering key papers and influences across research fronts (2009) 0.01
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    Abstract
    Gaining a rapid overview of an emerging scientific topic, sometimes called research fronts, is an increasingly common task due to the growing amount of interdisciplinary collaboration. Visual overviews that show temporal patterns of paper publication and citation links among papers can help researchers and analysts to see the rate of growth of topics, identify key papers, and understand influences across subdisciplines. This article applies a novel network-visualization tool based on meaningful layouts of nodes to present research fronts and show citation links that indicate influences across research fronts. To demonstrate the value of two-dimensional layouts with multiple regions and user control of link visibility, we conducted a design-oriented, preliminary case study with 6 domain experts over a 4-month period. The main benefits were being able (a) to easily identify key papers and see the increasing number of papers within a research front, and (b) to quickly see the strength and direction of influence across related research fronts.
  7. Fátima Loureiro, M. de: Information organization and visualization in cyberspace : interdisciplinary study based on concept maps (2007) 0.01
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    Source
    ¬La interdisciplinariedad y la transdisciplinariedad en la organización del conocimiento científico : actas del VIII Congreso ISKO-España, León, 18, 19 y 20 de Abril de 2007 : Interdisciplinarity and transdisciplinarity in the organization of scientific knowledge. Ed.: B. Rodriguez Bravo u. M.L Alvite Diez
  8. Christoforidis, A.; Heuwing, B.; Mandl, T.: Visualising topics in document collections : an analysis of the interpretation process of historians (2017) 0.01
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  9. Oh, K.E.; Halpern, D.; Tremaine, M.; Chiang, J.; Silver, D.; Bemis, K.: Blocked: when the information is hidden by the visualization (2016) 0.01
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    Abstract
    This study investigated how people comprehend three-dimensional (3D) visualizations and what properties of such visualizations affect comprehension. Participants were asked to draw the face of a 3D visualization after it was cut in half. We videotaped the participants as they drew, erased, verbalized their thoughts, gestured, and moved about a two-dimensional paper presentation of the 3D visualization. The videorecords were analyzed using a grounded theory approach to generate hypotheses related to comprehension difficulties and visualization properties. Our analysis of the results uncovered three properties that made problem solving more difficult for participants. These were: (a) cuts that were at an angle in relation to at least one plane of reference, (b) nonplanar properties of the features contained in the 3D visualizations including curved layers and v-shaped layers, and (c) mixed combinations of layers. In contrast, (a) cutting planes that were perpendicular or parallel to the 3D visualization diagram's planes of reference, (b) internal features that were flat/planar, and (c) homogeneous layers were easier to comprehend. This research has direct implications for the generation and use of 3D information visualizations in that it suggests design features to include and avoid.
  10. Jetter, H.-C.: Informationsvisualisierung und Visual Analytics (2023) 0.01
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    Abstract
    Die Visualisierung digitaler Datenbestände mit dem Computer ist heute alltäglich geworden. Spätestens seit der COVID-19-Pandemie sind computergenerierte Datenvisualisierungen und deren Interpretation durch den Menschen nicht mehr nur Expert*innen für Statistik und Datenanalyse vorbehalten. Stattdessen sind interaktive Visualisierungen zur Darstellung von Trends, Mustern oder Vergleichen in Daten zu festen Bestandteilen unseres medialen Alltags geworden, ob im (Daten-)Journalismus, in den sozialen Medien oder bei der Kommunikation von Behörden mit der Bevölkerung. Wie bereits von Reiterer und Jetter (2013) in einer früheren Auflage dieses Beitrags thematisiert wurde, bietet dieser Trend zur interaktiven und narrativen Visualisierung in den Massenmedien den Benutzer*innen neue Möglichkeiten des datenbasierten Erkenntnisgewinns. Seitdem popularisiert zusätzlich die Vielzahl verfügbarer "Tracker"-Apps mit dem Ziel der Verhaltensoptimierung (z. B. im Bereich Fitness oder Energiekonsum) die interaktive Visualisierung und Analyse persönlicher und privater Daten. Auch im beruflichen Alltag haben sich einstige Nischenwerkzeuge, wie z. B. die Visualisierungssoftware Tableau, in äußerst populäre Anwendungen verwandelt und sind zum Gegenstand zweistelliger Milliardeninvestitionen geworden, insbesondere für die Visualisierung und Analyse von Geschäftsdaten. Im Lichte dieser Entwicklungen soll dieser Beitrag daher im Folgenden einerseits grundlegende Begriffe und Konzepte der Informationsvisualisierung vermitteln, andererseits auch Alltagsformen und Zukunftstrends wie Visual Analytics thematisieren.
  11. Bekavac, B.; Herget, J.; Hierl, S.; Öttl, S.: Visualisierungskomponenten bei webbasierten Suchmaschinen : Methoden, Kriterien und ein Marktüberblick (2007) 0.01
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  12. Moya-Anegón, F. de; Vargas-Quesada, B.; Chinchilla-Rodríguez, Z.; Corera-Álvarez, E.; Munoz-Fernández, F.J.; Herrero-Solana, V.; SCImago Group: Visualizing the marrow of science (2007) 0.01
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  13. Julien, C.-A.; Leide, J.E.; Bouthillier, F.: Controlled user evaluations of information visualization interfaces for text retrieval : literature review and meta-analysis (2008) 0.01
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    Type
    b
  14. Wu, Y.; Bai, R.: ¬An event relationship model for knowledge organization and visualization (2017) 0.01
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    Abstract
    An event is a specific occurrence involving participants, which is a typed, n-ary association of entities or other events, each identified as a participant in a specific semantic role in the event (Pyysalo et al. 2012; Linguistic Data Consortium 2005). Event types may vary across domains. Representing relationships between events can facilitate the understanding of knowledge in complex systems (such as economic systems, human body, social systems). In the simplest form, an event can be represented as Entity A <Relation> Entity B. This paper evaluates several knowledge organization and visualization models and tools, such as concept maps (Cmap), topic maps (Ontopia), network analysis models (Gephi), and ontology (Protégé), then proposes an event relationship model that aims to integrate the strengths of these models, and can represent complex knowledge expressed in events and their relationships.
  15. Aletras, N.; Baldwin, T.; Lau, J.H.; Stevenson, M.: Evaluating topic representations for exploring document collections (2017) 0.00
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    Abstract
    Topic models have been shown to be a useful way of representing the content of large document collections, for example, via visualization interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a term list; that is the top-n words with highest conditional probability within the topic. Other topic representations such as textual and image labels also have been proposed. However, there has been no comparison of these alternative representations. In this article, we compare 3 different topic representations in a document retrieval task. Participants were asked to retrieve relevant documents based on predefined queries within a fixed time limit, presenting topics in one of the following modalities: (a) lists of terms, (b) textual phrase labels, and (c) image labels. Results show that textual labels are easier for users to interpret than are term lists and image labels. Moreover, the precision of retrieved documents for textual and image labels is comparable to the precision achieved by representing topics using term lists, demonstrating that labeling methods are an effective alternative topic representation.
  16. Wen, B.; Horlings, E.; Zouwen, M. van der; Besselaar, P. van den: Mapping science through bibliometric triangulation : an experimental approach applied to water research (2017) 0.00
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  17. Yan, B.; Luo, J.: Filtering patent maps for visualization of diversification paths of inventors and organizations (2017) 0.00
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  18. Ekström, B.: Trace data visualisation enquiry : a methodological coupling for studying information practices in relation to information systems (2022) 0.00
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  19. Platis, N. et al.: Visualization of uncertainty in tag clouds (2016) 0.00
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    Date
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Years

Languages

  • e 28
  • d 5
  • a 1
  • More… Less…

Types

  • a 26
  • m 5
  • el 3
  • x 2
  • b 1
  • s 1
  • More… Less…