The challenge of information visualization evaluation

By Catherine Plaisant. International Working Conference on Advanced Visual Interfaces, 2004

745 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/63938bfa8147f17265ec20073bc07f9eee371d5a

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Citations in the corpus

Interpreting informational cues: An explorative study on information use among prospective homebuyers (2009, neutral): “One of the challenges of research in information visualization is to assess the value of a given tool for a given task (Plaisant, 2004).”
Mastering the Information Age - Solving Problems with Visual Analytics (2010, background): “Plaisant[86] discusses the challenges of evaluation, also in the context of information visualisation.”
Task taxonomy for graph visualization (2006, background): “To improve the evaluation of information visualization systems, it is important to have benchmark datasets and tasks [6].”
Design Study Methodology: Reflections from the Trenches and the Stacks (2012, background): “The challenge of evaluating solutions against such fuzzy tasks is wellunderstood in the information visualization community [59].”
Exploratory Search: Beyond the Query-Response Paradigm (2009, methodology): “It has been suggested that repositories of data and tasks (similar to TREC) could be used to evaluate ESS based on information visualization (Plaisant, 2004).”
Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence (2014, background): “It is important that evaluations take real decision-making situations into account as well as possible (Perer and Shneiderman 2009; Plaisant 2004), although the difficulties of acquiring actual decision makers and following them in their actual, often long-term professional routines are well-known.”
Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models (2016, background): “measuring task time completion or number of errors) are often insufficient to evaluate visualization systems [6, 35, 38].”
An insight-based methodology for evaluating bioinformatics visualizations (2005, background): “However, it was difficult to judge the visualizations based on just the tasks and data sets [28].”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “ch often makes it difﬁcult to evaluate with controlled studies. Case studies, expert reviews, and focus groups are often used for evaluation of visual analytics and decision-support tools for experts [95,18]. Case studies aim to collect experts users feedback while performing high-level cognitive ta”
Evaluating Information Visualizations (2008, background): “One reason that it may be important to discuss the evaluation of information visualization, in general, is that it has been suggested that current evaluations are not convincing enough to encourage widespread adoption of information visualization tools [57].”
A survey on information visualization: recent advances and challenges (2014, methodology): “based on usability studies and controlled experiments [113].”
Casual Information Visualization: Depictions of Data in Everyday Life (2007, background): “Plaisant notes a similar set of challenges of infovis evaluation and contrasts four large categories of evaluation found in the infovis system literature [27].”
Analysis of biomedical data with multilevel glyphs (2014, methodology): “In the evaluation phase expert interviews at the work environment level based on methods described by Tory & Möller (2004) [32] and Plaisant (2004) [33] were done.”
Opportunities and Challenges for Data Physicalization (2015, neutral): “Given the focus on data representation, many of the evaluation issues are shared by the Visualization community [37, 50, 74].”
A Nested Model for Visualization Design and Validation (2009, methodology): “As we also discuss above, there has been significant previous work on evaluating visualization [9, 33, 42], but mostly with the focus on how to use the methods rather than when to use them.”
Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies (2006, methodology): “The reports of usability studies and controlled experiments are helpful but there is a growing desire for alternative methods of evaluation in order to present actionable evidence of measurable benefits that will encourage more widespread adoption of visualization [23].”
A Systematic Review on the Practice of Evaluating Visualization (2013, background): “In particular, empiric evaluation and the consideration of human factors are actively being discussed [3, 9, 10, 11, 19, 36, 46, 53, 65, 68].”