Nazemi, Kawa ; Burkhardt, Dirk ; Retz, Reimond ; Kuijper, Arjan ; Kohlhammer, Jörn (2014)
Adaptive Visualization of Linked-Data.
Advances in Visual Computing. 10th International Symposium, ISVC 2014.
doi: 10.1007/978-3-319-14364-4_84
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users' behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users' behavior. Our system does not require an initial expert modeling.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2014 |
| Autor(en): | Nazemi, Kawa ; Burkhardt, Dirk ; Retz, Reimond ; Kuijper, Arjan ; Kohlhammer, Jörn |
| Art des Eintrags: | Bibliographie |
| Titel: | Adaptive Visualization of Linked-Data |
| Sprache: | Englisch |
| Publikationsjahr: | 2014 |
| Verlag: | Springer, Berlin, Heidelberg, New York |
| Reihe: | Lecture Notes in Computer Science (LNCS); 8888 |
| Veranstaltungstitel: | Advances in Visual Computing. 10th International Symposium, ISVC 2014 |
| DOI: | 10.1007/978-3-319-14364-4_84 |
| Kurzbeschreibung (Abstract): | Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users' behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users' behavior. Our system does not require an initial expert modeling. |
| Freie Schlagworte: | Business Field: Visual decision support, Research Area: Human computer interaction (HCI), Adaptive visualization, Semantics visualization, Information visualization, Visual analytics, Linked open data (LOD) |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
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