Publications of the involved scientists
2012 |
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2. | Christian Stab; Matthias Breyer; Dirk Burkhardt; Kawa Nazemi; Jörn Kohlhammer Analytical semantics visualization for discovering latent signals in large text collections Proceedings Article In: Andreas Kerren; Stefan Seipel (Ed.): Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden, pp. 83–86, Linköping University Linköping University Electronic Press, 2012, ISBN: 978-91-7519-723-4. @inproceedings{stab2012analytical, Considering the increasing pressure of competition and high dynamics of markets; the early identification and specific handling of novel developments and trends becomes more and more important for competitive companies. Today; those signals are encoded in large amounts of textual data like competitors’ web sites; news articles; scientific publications or blog entries which are freely available in the web. Processing large amounts of textual data is still a tremendous challenge for current business analysts and strategic decision makers. Although current information systems are able to process that amount of data and provide a wide range of information retrieval tools; it is almost impossible to keep track of each thread or opportunity. The presented approach combines semantic search and data mining techniques with interactive visualizations for analyzing and identifying weak signals in large text collections. Beside visual summarization tools; it includes an enhanced trend visualization that supports analysts in identifying latent topic-related relations between competitors and their temporal relevance. It includes a graph-based visualization tool for representing relations identified during semantic analysis. The interaction design allows analysts to verify their retrieved hypothesis by exploring the documents that are responsible for the current view. |
2011 |
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1. | Kawa Nazemi; Matthias Breyer; Arjan Kuijper User-Oriented Graph Visualization Taxonomy: A Data-Oriented Examination of Visual Features Conference Human Centered Design, LNCS 6776 Springer Berlin Heidelberg, 2011, ISBN: 978-3-642-21753-1. @conference{C35-P-22203, Presenting information in a user-oriented way has a significant impact on the success and comprehensibility of data visualizations. In order to correctly and comprehensibly visualize data in a user-oriented way data specific aspects have to be considered. Furthermore, user-oriented perception characteristics are decisive for the fast and proper interpretation of the visualized data. In this paper we present a taxonomy for graph visualization techniques. On the one hand it provides the user-oriented identification of applicable visual features for given data to be visualized. On the other hand the set of visualization techniques is enclosed which supports these identified visual features. Thus, the taxonomy supports the development of user-oriented visualizations by examination of data to obtain a beneficial association of data to visual features. |