scholarly journals An empirical comparison of three commercial information visualization systems

2001 ◽  
Author(s):  
A. Kobsa
Keyword(s):  
Information Visualization ◽  
Empirical Comparison ◽  
Visualization Systems

Development of Adaptive Information Visualization Systems with Augmented Reality

2014 ◽  
Author(s):  
Ezequiel R. Zorzal ◽  
Celso A.R. de Sousa ◽  
Alexandre Cardoso ◽  
Claudio Kirner ◽  
Edgard A. Lamouner ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Information Visualization ◽  
Visualization Systems

scholarly journals The Development of Heuristics for Evaluation of Dashboard Visualizations

2018 ◽  
Vol 09 (03) ◽  
pp. 511-518 ◽  
Author(s):  
Dawn Dowding ◽  
Jacqueline Merrill
Keyword(s):  
Health Care ◽  
Information Visualization ◽  
Nominal Group ◽  
Task Completion ◽  
Heuristic Evaluation ◽  
Data Systems ◽  
Task List ◽  
Usability Problems ◽  
Visualization Systems ◽  
Information Visualizations

Background Heuristic evaluation is used in human–computer interaction studies to assess the usability of information systems. Nielsen's widely used heuristics, first developed in 1990, are appropriate for general usability but do not specifically address usability in systems that produce information visualizations. Objective This article develops a heuristic evaluation checklist that can be used to evaluate systems that produce information visualizations. Principles from Nielsen's heuristics were combined with heuristic principles developed by prior researchers specifically to evaluate information visualization. Methods We used nominal group technique to determine an appropriate final set. The combined existing usability principles and associated factors were distributed via email to a group of 12 informatics experts from a range of health care disciplines. Respondents were asked to rate each factor on its importance as an evaluation heuristic for visualization systems on a scale from 1 (definitely don't include) to 10 (definitely include). The distribution of scores for each item were calculated. A median score of ≥8 represented consensus for inclusion in the final checklist. Results Ten of 12 experts responded with rankings and written comments. The final checklist consists of 10 usability principles (7 general and 3 specific to information visualization) substantiated by 49 usability factors. Three nursing informatics experts then used the checklist to evaluate a vital sign dashboard developed for home care nurses, using a task list designed to explore the full functionality of the dashboard. The experts used the checklist without difficulty, and indicated that it covered all major usability problems encountered during task completion. Conclusion The growing capacity to generate and electronically process health data suggests that data visualization will be increasingly important. A checklist of usability heuristics for evaluating information visualization systems can contribute to assuring high quality in electronic data systems developed for health care.

scholarly journals Task-based assessment of visualization tools for the comparison of biological taxonomies

2018 ◽  
Vol 4 ◽  
pp. e25742 ◽  
Author(s):  
Lilliana Sancho-Chavarria ◽  
Fabian Beck ◽  
Daniel Weiskopf ◽  
Erick Mata-Montero
Keyword(s):  
Information Visualization ◽  
Matrix Representations ◽  
Complex Information ◽  
Analytical Reasoning ◽  
Fundamental Information ◽  
Visualization Tools ◽  
Similarities And Differences ◽  
Visualization Systems ◽  
Global Initiatives ◽  
Visual Interfaces

Maintenance and curation of large-sized biological taxonomies are complex and laborious activities. Information visualization systems use interactive visual interfaces to facilitate analytical reasoning on complex information. Several approaches such as treemaps, indented lists, cone trees, radial trees, and many others have been used to visualize and analyze a single taxonomy. In addition, methods such as edge drawing, animation, and matrix representations have been used for comparing trees. Visualizing similarities and differences between two or more large taxonomies is harder than the visualization of a single taxonomy. On one hand, less space is available on the screen to display each tree; on the other hand, differences should be highlighted. The comparison of two alternative taxonomies and the analysis of a taxonomy as it evolves over time provide fundamental information to taxonomists and global initiatives that promote standardization and integration of taxonomic databases to better document biodiversity and support its conservation. In this work we assess how ten user visualization tasks for the curation of biological taxonomies are supported by several visualization tools. Tasks include the identification of conditions such as congruent taxa, splits, merges, and new species added to a taxonomy. We consider tools that have gone beyond the prototype stage, that have been described in peer-reviewed publications, or are in current use. We conclude with the identification of challenges for future development of taxonomy comparison tools.

scholarly journals Predicting Visual Search Task Success from Eye Gaze Data as a Basis for User-Adaptive Information Visualization Systems

2021 ◽  
Vol 11 (2) ◽  
pp. 1-25
Author(s):  
Moritz Spiller ◽  
Ying-Hsang Liu ◽  
Md Zakir Hossain ◽  
Tom Gedeon ◽  
Julia Geissler ◽  
...  
Keyword(s):  
Visual Search ◽  
Computational Model ◽  
Information Visualization ◽  
Adaptive Systems ◽  
Short Term Memory ◽  
Eye Gaze ◽  
Research Literature ◽  
Convolutional Network ◽  
Visualization Systems ◽  
Information Visualizations

Information visualizations are an efficient means to support the users in understanding large amounts of complex, interconnected data; user comprehension, however, depends on individual factors such as their cognitive abilities. The research literature provides evidence that user-adaptive information visualizations positively impact the users’ performance in visualization tasks. This study attempts to contribute toward the development of a computational model to predict the users’ success in visual search tasks from eye gaze data and thereby drive such user-adaptive systems. State-of-the-art deep learning models for time series classification have been trained on sequential eye gaze data obtained from 40 study participants’ interaction with a circular and an organizational graph. The results suggest that such models yield higher accuracy than a baseline classifier and previously used models for this purpose. In particular, a Multivariate Long Short Term Memory Fully Convolutional Network shows encouraging performance for its use in online user-adaptive systems. Given this finding, such a computational model can infer the users’ need for support during interaction with a graph and trigger appropriate interventions in user-adaptive information visualization systems. This facilitates the design of such systems since further interaction data like mouse clicks is not required.

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