exposing trends and relationships to support sensemaking within faceted datasets

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Bongshin Lee, Greg Smith, George Robertson, Mary Czerwinski, Desney Tan Computational User Experiences (CUE) Visualization and Interaction Research Group Microsoft Research Exposing Trends and Relationships to Support Sensemaking within Faceted Datasets FacetLens

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FacetLens. Exposing Trends and Relationships to Support Sensemaking within Faceted Datasets. Bongshin Lee, Greg Smith, George Robertson, Mary Czerwinski, Desney Tan Computational User Experiences (CUE) Visualization and Interaction Research Group Microsoft Research. Motivation. - PowerPoint PPT Presentation

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Page 1: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Bongshin Lee, Greg Smith, George Robertson, Mary Czerwinski, Desney Tan

Computational User Experiences (CUE)Visualization and Interaction Research Group

Microsoft Research

Exposing Trends and Relationships to Support Sensemaking within Faceted

Datasets

FacetLens

Page 2: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

MotivationExplore large metadata-rich collections of information

Provide a more effective and enjoyable searching and browsing user experience with faceted browsing

Show meaningful trends in dataShow relationship between items

Page 3: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Who has Brad published with?

What InfoVis papers did Stu, Jock, and George co-author in 1991?

How many times has Hiroshi been cited by CHI papers?

How has Ben’s publication pattern changed over the years?

History of CHI: Pop Quiz

Page 4: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Faceted Browsing

Facet: Grouping of item attributesMultiple paths to any item

Faceted Browsing: Integration of facets with dynamic query previews

Studies have shown performance and preference advantages (e.g., Flamenco, CHI ’03)

Page 5: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Faceted Browsing

FacetMap [InfoVis ‘06]Flamenco [CHI ‘03] Relation Browser++[Digital Gov. Research

‘05]

+ many commercial online shopping sites

Page 6: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Trend VisualizationMultiple bar charts

IN-SPIRE [InfoVis ‘04]PaperLens [CHI ‘05]NetLens [IVS ‘07]

Stacked bar chartsThemeRiver [TVCG ‘02]NameVoyager [InfoVis ‘05]Stacked Graphs [InfoVIs ‘08]

+ many commercial tools (e.g., Microsoft Excel, Tableau [InfoVis ‘07], ...)

Page 7: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Trend Visualization: Facet-based

InfoZoom [DM ‘00]Relation Browser++[Digital Gov. Research

‘05]

Bungee Viewhttp://cityscape.inf.cs.cmu.edu/

bungee/

Page 8: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

FacetLens =

Faceted Browsing

Trend Vis

+ + Additional features…

Page 9: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

FacetLens: Linear FacetsIdentify and Compare Trends

Attribute values are visually presented in an explicit order (e.g., time)

Page 10: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

FacetLens: PivotingNavigate between Related Items

navigate further into related items whether or not filters have been exhausted

Enabled by allowing items to be attributes of other items

Page 11: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

FacetLens: Multi-value FacetReveal Relationships

Attribute co-occurrenceCo-authorshipCo-citations

When an item can have multiple simultaneous values for the same facet

Page 12: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

FacetLens: Attribute Value SearchScaling to Large Datasets

All the attribute values often cannot fit in the allocated screen space

Page 13: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Evaluation: Expert Use2 datasets

ACM CHI Publications23 years (1982-2004)4073 papers & 6300 authorsTopics, Authorship, Affiliations, …

Find insightsInitial overviewSimple filteringFurther exploration

OECD grant data32 years (1974-2005)about 1M inter-county grantsDonors, Recipients, Purposes, …

Demo

Page 14: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets
Page 15: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Usability Study: Novice Use5 researchers and 1 developer (2 females)ACMCHI Publications dataset9 tasks

begin with simple tasks and then gradually increase complexity2 tasks consist of 2 sub-tasks1 task consists of 3 sub-tasks

Lasted about 30 minutes

Page 16: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

1. Which author publishes the most frequently in the topic of CSCW?

2. What are the affiliations of the two authors from China?3. a. How many papers did Irene Grief author?

b. Which year did she publish the most?4. Which co-author did Brad Myers publish with most

frequently?5. In what year was Hiroshi Ishii cited by the most papers?6. a. Who was Ben Shneiderman’s most frequent coauthor in

the Info Vis topic area?b. How many papers did they publish together in Info Vis?c. Which year did they publish the most together?

7. Compare the publication trends for Lab Reports by year and CSCW papers by year. How do they differ?

8. a. Find the authors of the paper “The Perspective Wall.” Which states did they live in?b. Pivot to the papers authored by Jock Mackinlay. Which paper did his papers cite the most?

9. Describe three facts about Randy Pausch.

User study tasks

Page 17: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Results

Overall, easy to use

Easy, Useful and Interesting

Trend visualizationDrag and drop interactionAttribute search within the facets

Missing: undo

Semantics of facets are difficult to express

Symmetric relationshipMulti-value facets

Page 18: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

ConclusionFacetLens supports exploration of a large faceted datasets by exposing trends and relationships

Future WorkImprove navigationPaper & Author vs. Paper-centricScale to larger datasetsLongitudinal study

Page 19: Exposing Trends and Relationships  to Support Sensemaking within Faceted Datasets

Thank You! http://research.microsoft.com/cue/facetlens

Questions?