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Eibl, M.; Mandl, T.: ¬Die Qualität von Visualisierungen : eine Methode zum Vergleich zweidimensionaler Karten (2004)
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- Date
- 29. 1.2005 19:17:50
- Pages
- S.89-115
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Linden, E.J. van der; Vliegen, R.; Wijk, J.J. van: Visual Universal Decimal Classification (2007)
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Reiterer, H.; Jetter, H.-C.: ¬Das Projekt Mediovis : visuelle Exploration digitaler Bibliotheken (2007)
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Trentin, G.: Graphic tools for knowledge representation and informal problem-based learning in professional online communities (2007)
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Shiri, A.; Molberg, K.: Interfaces to knowledge organization systems in Canadian digital library collections (2005)
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Osiñska, V.: Visual analysis of classification scheme (2010)
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- 6. 1.2011 19:29:12
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- Knowledge organization. 37(2010) no.4, S.299-306
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Mercun, T.; Zumer, M.; Aalberg, T.: Presenting bibliographic families : Designing an FRBR-based prototype using information visualization (2016)
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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)
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- Journal of the Association for Information Science and Technology. 68(2017) no.3, S.724-738
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Chen, C.: CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature (2006)
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Wu, K.-C.; Hsieh, T.-Y.: Affective choosing of clustering and categorization representations in e-book interfaces (2016)
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Wu, I.-C.; Vakkari, P.: Effects of subject-oriented visualization tools on search by novices and intermediates (2018)
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Osinska, V.; Kowalska, M.; Osinski, Z.: ¬The role of visualization in the shaping and exploration of the individual information space : part 1 (2018)
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- Knowledge organization. 45(2018) no.7, S.547-558
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Chowdhury, S.; Chowdhury, G.G.: Using DDC to create a visual knowledge map as an aid to online information retrieval (2004)
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- Content
- 1. Introduction Web search engines and digital libraries usually expect the users to use search terms that most accurately represent their information needs. Finding the most appropriate search terms to represent an information need is an age old problem in information retrieval. Keyword or phrase search may produce good search results as long as the search terms or phrase(s) match those used by the authors and have been chosen for indexing by the concerned information retrieval system. Since this does not always happen, a large number of false drops are produced by information retrieval systems. The retrieval results become worse in very large systems that deal with millions of records, such as the Web search engines and digital libraries. Vocabulary control tools are used to improve the performance of text retrieval systems. Thesauri, the most common type of vocabulary control tool used in information retrieval, appeared in the late fifties, designed for use with the emerging post-coordinate indexing systems of that time. They are used to exert terminology control in indexing, and to aid in searching by allowing the searcher to select appropriate search terms. A large volume of literature exists describing the design features, and experiments with the use, of thesauri in various types of information retrieval systems (see for example, Furnas et.al., 1987; Bates, 1986, 1998; Milstead, 1997, and Shiri et al., 2002).
- Date
- 29. 8.2004 13:37:50
- Pages
- S.133-138
-
Spero, S.: LCSH is to thesaurus as doorbell is to mammal : visualizing structural problems in the Library of Congress Subject Headings (2008)
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- Pages
- S.203
- 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
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Wu, I.-C.; Vakkari, P.: Supporting navigation in Wikipedia by information visualization : extended evaluation measures (2014)
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- Date
- 8. 4.2015 16:20:29
- Source
- Journal of documentation. 70(2014) no.3, S.392-424
-
Petrovich, E.: Science mapping and science maps (2021)
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- Date
- 27. 5.2022 18:19:29
- Source
- Knowledge organization. 48(2021) no.7/8, S.535-562
-
Batorowska, H.; Kaminska-Czubala, B.: Information retrieval support : visualisation of the information space of a document (2014)
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- Pages
- S.343-350
- 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
-
Information visualization in data mining and knowledge discovery (2002)
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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.
- Pages
- xiii, 407 S
- Type
- s
-
Dynamische Visualisierung : Grundlagen und Anwendungsbeispiele für kartographische Animationen (2000)
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- Pages
- XII, 234 S
-
Saß, J.: Bestandsvisualisierung in Bibliotheken (2015)
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- Source
- Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 68(2015) H.3/4, S.439-457
