1 i247: Information Visualization and Presentation Marti Hearst April 28, 2008

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i247: Information Visualization and PresentationMarti Hearst

April 28, 2008 

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Today

• Recent Infoviz Research Results: CHI 2008• Topics:

– Three papers on collaborative visualization– Two papers on exploratory data analysis– Solving hard problems

• Understanding social networks• Understanding errors in CPU networks

– Foundations• Intelligent selection• Distortion-based technique

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Do Visualizations Improve Synchronous Remote Collaboration?

• Balakrishnan, Fussell, Kiesler• Will viz in collaboration improve outcome?

– Crime-solving task– Social Network visualization– 94 (!) participants, randomly paired– Four conditions

No Viz

Each had ½ data,Unshared viz

Each had ½ data,Could see other

person’s View of viz Full shared

accessto viz

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NetDraw for Crime Analysis

• Goal: find the serial killer• Distracter task: a recent single murder

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Shared Viz Results

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More Detail on Results

• The more the pairs discussed the network, the more they discussed the serial killer. In turn, the more they discussed serial killer, the more likely they were to solve the problem.

• Shared-only view was relatively unhelpful.– Maybe a distraction to look at both views at once.

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An Exploratory Study of Visual Information Analysis

• Isenberg, Tang, Carpendale• Question:

– How do people work in teams using visualizations when there are no tool constraints?

• Method:– 24 participants from university, mean age 26– Gave people paper cards with standard visualizations– People were in groups of singles, pairs, triples– Assigned them tasks from 2 standard datasets

• Who should each breakfast option be advertised to?• Where is it most appropriate to laugh?

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Methods of Using Static Viz’s

• Results were similar to the old sensemaking work– People moved fluidly between different modes of

interaction (browsing, selecting, organizing, etc)– Nothing really new here

• Communication results– 8 teams discussed their collaboration style at the

beginning.• Task division, parallel work on same tasks, joint work

– NONE of these followed the agreed-upon style!• Mainly did parallel work.

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Your Place or Mine?Visualization as a Community Component

• Danis, Viegas, Wattenberg Kriss• Question:

– Where is all the interaction happening with the ManyEyes community visualization website?

• Approach: – Interview 20 users

• 10 bloggers• 10 other users

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Where is ManyEyes Used?• People used the tool within their own spaces

– On a personal blog– Part of a debate in an online forum– As an internal discussion for decision making

• Put a tag cloud on a report cover– For pedagogical purposes

• One teacher said it was good for getting the conversation going

• Another said it was good to alleviate fears of quantitative data

• Question not asked:– Perhaps their community interaction system wasn’t as

good as it could be• Sense.us has better interactions, imho

– They’ve launched an improved version and that may change things.

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Integrating Statistics and Visualization:Case Studies of Gaining Clarity during Exploratory Data Analysis

• Perer & Shneiderman• Issue:

– network viz’s are not all that helpful for analyzing social networks

– Statistics alone are also not that helpful

• Solution:– Do a better job of integrating them

• Issue:– It’s hard to really see what works for exploratory data

analysis

• Solution:– Get serious about longitudinal studies– Also, make sure you have highly-motivated, realistic users

testing it.

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Case Study: Political Analyst• Studying Senatorial Power Structures, based

on whom the voted with

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Political Analyst Case Study

• First 6 months of 2007, Senate votes• Analyst had tried 3 other network analysis

tools, without gaining the insight he wanted– NetDraw, ManyEyes, and KrackPlot

• Found some interesting patterns using the capacity to rank all nodes, visualize outcome and then filter out the unimportant– The betweenness centrality statistic helped find

“centers of gravity”– Found geographic alliances.

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Case Study: Evaluating a Recommender System Algorithm

• Lin had ground truth from the TREC Genomics track; looked at how well the PubMed “related papers” worked for 50 queries.

• Asked for special features such as the ability to calculate the number of relevant documents linked from each relevant document.

• Outcome: a better understanding of the algorithm and a high-quality research paper.

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Case Study: Find Power Relationships Among Hospitals and Healthcare Provides

• An outlier was found to be both a gatekeeper (high betweenness) but only 4 connections.

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Case Study: Counter-terrorism• Friendship network vs religious network• Color shows rank by in-degree

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LiveRAC: Interactive Visual Exploration of System Management Time-Series Data

• McLachlan, Munzner, Koutsofios, North• Problem:

– managing large numbers of networked computer systems (outsourced IT infrastructure)

• Method:– Case study of phased approach

• halfway between full-scale user-centered design and

– Informal qualitative study of in a production setting• encouraged feedback on design approach and

visualization design principles

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Limitations of Current Tools

• Most significant limitation: lack of mid-level overviews– high level overviews are useful, but have limitations– what do these numbers mean? which systems are

up, which are down? how important are they? which customers are affected?

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Large Sets of Time Series

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LiveRAC: Main Techniques

• Borrowed from Tablelens:– Sorting of columns – Enlarging cells keeps them open

• More sophisticated semantic zoom– Shows several levels of detail simultaneously

• Sparklines for temporal data • Tightly-coupled small multiples with side-by-

side comparison• Video:

http://www.cs.ubc.ca/labs/imager/video/2007/liverac/liverac.mov

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Generalized Selection via Interactive Query Relaxation• Heer, Agrawala, and Willett• Idea:

– Selecting regions or attributes can be more useful than selecting individual items

– Related to the sense.us social visualization work in that consistent selection is important

• Video:http://vis.berkeley.edu/papers/generalized_selection/2008-GeneralizedSelection-CHI.mov

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Expandable Grids for Visualizing and Authoring Computer Security Policies

Reeder et al.

• Issue:– It is an unsolved problem of how to design interfaces

for setting interlocking preference rules– Also called policy authoring– These include: ubiquitous computing rules, security

policies (such as file permissions settings)– List-of-rules method is flawed; doesn’t show

interactions, among other things

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Expandable Grids for Visualizing and Authoring Computer Security Policies

Reeder et al.

• Idea:– List-of-rules method is flawed; doesn’t show

interactions, among other things.– Instead, use a matrix view with

• Principals (in a hierarchy) on one axis• Resources (in a hierarchy) on the other axis• Interactions are shown in colored cells

– Shows a holistic view of the outcome of applying rules

– Uses highlighting (via crosshairs)– Allows for search

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P3P Policies

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Usability Study Results

• For a file permissions task:– “Set permissions so that Jana can read and write the

FPH.doc file in the Theory\Handouts folder.– (they did change a policy rule from Windows’)– 36 technical undergraduates– Between-participants design

• With Windows XP UI: 6% accuracy• With Grid: 100% accuracy

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PeerChooser: Visual Interactive RecommendationO’Donovan et al.Use a graph to tweak your recommendations

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Evaluation

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Evaluation

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Melange: Space folding for Multi-focus Interaction

• Elmqvist et al.• Idea:

– Distortion technique that folds intermediate space to allow visibility of multiple focal regions

http://www.lri.fr/~elm/media/melange-1.pnghttp://www.lri.fr/~elm/media/melange-2.pnghttp://www.lri.fr/~elm/media/melange-3.png– Usability study showed slight, if any, benefit for a

standard view, and only 5 out of 12 preferred it.