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Modeling The User. Klaus Mueller (and Joachim Giesen, MPI). Klaus Mueller. Computer Science Center for Visual Computing Stony Brook University. Dagstuhl 2007 Moments. Dagstuhl 2007 Moments. Traumatizing beginnings: Edi Gröller: “Kill (Eliminate) the user!”. Dagstuhl 2007 Moments.
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Modeling The User Klaus Mueller (and Joachim Giesen, MPI) Klaus Mueller Computer Science Center for Visual Computing Stony Brook University
Dagstuhl 2007 Moments • Traumatizing beginnings: • Edi Gröller: “Kill (Eliminate) the user!”
Dagstuhl 2007 Moments • Traumatizing beginnings: • Edi Gröller: “Kill (Eliminate) the user!” • Regaining hope: • Tom Ertl: “User studies are needed.” • Chuck Hansen: dedicates 1 out of his 4 talks to user studies
Dagstuhl 2007 Moments • Traumatizing beginnings: • Edi Gröller: “Kill (Eliminate) the user!” • Regaining hope: • Tom Ertl: “User studies are needed.” • Chuck Hansen: dedicates 1 out of his 4 talks to user studies • Inspiring thoughts (in unrelated context): • Penny Rheingans: “Improve visualization accuracy by creating a model of the data.”
Overall Tone: • User studies are sorely needed, but hard to do!!
Now, Why Are User Studies So Hard? • Visualization algorithms typically have many parameters, each with many value settings • the sheer permutation complexity can be overwhelming
Now, Why Are User Studies So Hard? • Visualization algorithms typically have many parameters, each with many value settings • the sheer permutation complexity can be overwhelming • Testing them all on one user may actually lead to his death
Now, Why Are User Studies So Hard? • Visualization algorithms typically have many parameters, each with many value settings • the sheer permutation complexity can be overwhelming • Testing them all on one user may actually lead to his death • and we need to perform these tests with many users
Now, Why Are User Studies So Hard? • Visualization algorithms typically have many parameters, each with many value settings • the sheer permutation complexity can be overwhelming • Testing them all on one user may actually lead to his death • and we need to perform these tests with many users • So… Mission Accomplished?
Well… • Let’s have a closer look…
For Example: Volume Rendering • Some rather trivial parameters: • rendering algorithm (X-ray, MIP, US-DVR, S-DVR, GW-DVR) • ray step size (continuous scale) • resolution • background • colormap (color transfer function) • viewpoint
For Example: Volume Rendering • Some rather trivial parameters: • rendering algorithm (X-ray, MIP, US-DVR, S-DVR, GW-DVR) • ray step size (continuous scale) • resolution • background • colormap (color transfer function) • viewpoint • Some more complex ones: • rendering style (various illustrative rendering schemes) • (magic) lenses • (magic) shadows • advanced BRDFs and ray modeling • etc…
Sample Testing Scenario (1) • Which colormap shows more detail?
Sample Testing Scenario (2) • Which colormap shows more detail?
Parameter Test Complexity • Notice: • all renderings show all features • all renderings use the same window size • Variables: • 3 colormaps • 5 rendering modes • 6 viewpoints • 2 image resolutions • 3 ray step sizes • 5 backgrounds 2700 permutations 7M pair-wise comparisons
Daunting, But Not Unusual… • Market research deals with these problems on a regular basis • have attributes (parameters) and levels (values)
Daunting, But Not Unusual… • Market research deals with these problems on a regular basis • have attributes (parameters) and levels (values) • For example, consider the design of a new car model, optimizing the following parameters: • comfort and convenience • quality • styling • performance
Daunting, But Not Unusual… • Market research deals with these problems on a regular basis • have attributes (parameters) and levels (values) • For example, consider the design of a new car model, optimizing the following parameters: • comfort and convenience • quality • styling • performance • Sounds familiar?
How Can This Help Us? • A common technique used in market research is conjoint analysis • Conjoint analysis allows one to: • interview a modest number of people • with a modest number of pair-wise comparison tests • The tests simulate real buying situations and statistical significance can be determined
How Can This Help Us? • A common technique used in market research is conjoint analysis • Conjoint analysis allows one to: • interview a modest number of people • with a modest number of pair-wise comparison tests • The tests simulate real buying situations and statistical significance can be determined • We have actually done this: • 786 respondents • 20 pair-wise tests each
How Can This Help Us? • A common technique used in market research is conjoint analysis • Conjoint analysis allows one to: • interview a modest number of people • with a modest number of pair-wise comparison tests • The tests simulate real buying situations and statistical significance can be determined • We have actually done this: • 786 respondents • 20 pair-wise tests each • And the results make sense
Results • Top 10 (detail / aesthetics):
Results • Top 10 (detail / aesthetics): • Flop 10 (detail / aesthetics):
Method • We apply Thurstone’s Method of Comparative Judgment to each attribute separately • isolate attributes in the choice tasks • determine relative rankings of attribute levels using the frequency a level was chosen over another • assume normal distributed rankings (and their differences) • Conjoint structure requires a modification of Thurstone’s method • the rankings of the various attributes must be transformed into a comparable scale • the transformation factor marks the relative influence of this attribute on the overall visualization experience
What Does This Enable? • Efficient testing of multi-parameter scenarios in visualization
What Does This Enable? • Efficient testing of multi-parameter scenarios in visualization • Personalization of visualization experiences for specific users (or user groups)
What Does This Enable? • Efficient testing of multi-parameter scenarios in visualization • Personalization of visualization experiences for specific users (or user groups) • Learning of user preferences given specific task and rendering scenario descriptions
What Does This Enable? • Efficient testing of multi-parameter scenarios in visualization • Personalization of visualization experiences for specific users (or user groups) • Learning of user preferences given specific task and rendering scenario descriptions • Constructing a model of the user to optimize his/her visualization experiences and efficiency
Acknowledgments • Lujin Wang (Stony Brook University) • for rendering 5000+ images • Eva Schuberth (ETH Zürich) • for contributing on the statistics • Peter Zolliker (EMPA Dübendorf) • for contributing on perceptional issues and stats
Questions? • Which image do you like best? • Which image shows more detail?