Developing Qualitative Metrics for Visual Analytic Environments

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Presentation

Notes

- focus on qualitative metrics on utility than usability

- interested analytic process, use of vis in this process, quality of results of process

- VAST developed data sets with ground truths, know you are right

- review of all the reviews submitted for mini challenges (42)

- asked three questions, focused on second

- material ok

- (focus) what reviewers think are important

- selecting 2 types of reviewers: prof analysts (hard), vis experts


- reviewers given video submission, screen shots, text descriptions

- reviewers comment on clarity, clear the better scores obtained, not clear, only 5% get a score higher than 5

- comments analytical, vis


- why select a particular vis; intuitive to analysis

- comment on vis: complexity, too complex, can't see answer, find line between complexity of vis and putting enough for people to what's happening

- people don't like having to mouse

- careful about showing relationships

- comparison is very important


q and a

- how many reviewers

- comments available to next year's contestants

- comments may be too prescriptive, only put comments there that have been made multiple times (no frequency statistics)

- some comments may not be right, but it's a starting point

- have to be careful as to how to phrase it, shouldn't be applied without thinking about it

- video quality, infovis/vast high correlation between acceptance and availability of video; in challenge--explanation is more correlated than with videos

- metrics of the contest in the future that reviewers will use

- different data--different comments this year

- accessibility--not explicitly stated: guidelines or metrics given; not catered to color-blind / blind people;

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