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Adaptive Visualization of Linked-Data

Nazemi, Kawa and Burkhardt, Dirk and Retz, Reimond and Kuijper, Arjan and Kohlhammer, Jörn (2014):
Adaptive Visualization of Linked-Data.
Springer, Berlin, Heidelberg, New York, In: Advances in Visual Computing. 10th International Symposium, ISVC 2014, In: Lecture Notes in Computer Science (LNCS); 8888, DOI: 10.1007/978-3-319-14364-4₈₄, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Nazemi, Kawa and Burkhardt, Dirk and Retz, Reimond and Kuijper, Arjan and Kohlhammer, Jörn
Title: Adaptive Visualization of Linked-Data
Language: English
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.

Series Name: Lecture Notes in Computer Science (LNCS); 8888
Publisher: Springer, Berlin, Heidelberg, New York
Uncontrolled Keywords: Business Field: Visual decision support, Research Area: Human computer interaction (HCI), Adaptive visualization, Semantics visualization, Information visualization, Visual analytics, Linked open data (LOD)
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Advances in Visual Computing. 10th International Symposium, ISVC 2014
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/978-3-319-14364-4₈₄
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