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›› ExPlates ›› PivotLab ›› PurdueUniversity
››
West Lafayette, IN, USA
»EuroVis2013»June17-21»LeipZig,Germany
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
Life is a journey, not a destination.
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
“Life is a journey, not a destination.”
― Ralph Waldo Emerson (1803-1882)
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
visual exploration [ˈvɪʒʊəl -zjʊ- ˈɛkspləˈreɪʃən], n.
using visualization to analyze data, often without
prior knowledge or questions about the data
›› ExPlates ›› PivotLab ›› PurdueUniversity
›› GOAL
» Support visual exploration by spatializing
the interaction
» Time → Space
» Externalizes not just the data,
but also the exploration process
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
PREVIEW
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
Why is this important?
Why is this difficult?
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
» Perception: many views
yield high visual clutter
» Memory: remembering
past choices and results
» Reasoning: synthesizing multiple
disparate findings is difficult
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
›› ExPlates ›› PivotLab ›› PurdueUniversity
›› Exploration Plates (ExPlates)
» Data-flow method for visualization that
automatically spatializes interaction
10
Spatialize…
›› ExPlates ›› PivotLab ›› PurdueUniversity
›› Plate Anatomy
» Building block: exploration plate
– Visualization state: data, mapping, view
– Input and output ports (anchors)
– Connected by wires
» Mutating ops create new plate(s)
– Filtering, change visualization, transforms
» Invariant ops update current plate
– Color scale, viewport, formatting
11
›› ExPlates ›› PivotLab ›› PurdueUniversity
›› Plate Types
» Visualization plates: visual
representations of input data
» Data plates: data transformations from
input to output
» Annotation plates: add annotation to
specific locations on the canvas
12
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
Output
anchors
Input
anchors
Control
area
Visualization
area
Data
wires
›› ExPlates ›› PivotLab ›› PurdueUniversity
›› Canvas and Layout
» Infinitely zoomable visual canvas
– Mouse control + automatic operations
» Grid-based semi-automatic layout
– Padding for data wires
» Two ways to create new plates
– Manual (menu) or automatic (spatializing)
14
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
DEMO
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
IMPLEMENTATION
›› ExPlates ›› PivotLab ›› PurdueUniversity
›› Implementation
» Web-based system (JavaScript + SVG)
» Google Data Source API
– Google Docs (spreadsheets)
– RSS/Atom feeds
– XML files
– CSV files
» Rendering: RaphaëlJS (raphaeljs.com)
– Extensible with other SVG toolkits (D3, etc)
17
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
DISCUSSION
›› ExPlates ›› PivotLab ›› PurdueUniversity
›› Discussion and Limitations
» Scalability: complex exploration + size
– Zooming and panning navigation
– Web-based setting gives upper bound
» Expertise: web-based but not intended
for novice-level users
» Comparison: relation to MDV tools
– Data-flow (DataMeadow, GraphTrail)
– Dashboard/workbench (Tableau, Spotfire)
20
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
CONCLUSION
›› ExPlates ›› PivotLab ›› PurdueUniversity
›› » Spatializing exploration
– Branching visual history
– Duplicate, do not update
» Data flow system
– Automatic layout
» Multidimensional data
– Visualization + analysis
» Web-based prototype
– Live, dynamic updates
›› ExPlates ›› PivotLab ›› PurdueUniversity
››
Questions?
Niklas Elmqvist
Purdue University
West Lafayette, IN, USA
elm@purdue.edu
»EuroVis2013»June17-21»LeipZig,Germany
All images are Creative
Commons from Flickr.com

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ExPlates: Spatializing Interactive Analysis to Scaffold Visual Exploration

  • 1. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› West Lafayette, IN, USA »EuroVis2013»June17-21»LeipZig,Germany
  • 2. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Life is a journey, not a destination.
  • 3. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› “Life is a journey, not a destination.” ― Ralph Waldo Emerson (1803-1882)
  • 4. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› visual exploration [ˈvɪʒʊəl -zjʊ- ˈɛkspləˈreɪʃən], n. using visualization to analyze data, often without prior knowledge or questions about the data
  • 5. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› GOAL » Support visual exploration by spatializing the interaction » Time → Space » Externalizes not just the data, but also the exploration process
  • 6. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› PREVIEW
  • 7. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Why is this important? Why is this difficult?
  • 8. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› » Perception: many views yield high visual clutter » Memory: remembering past choices and results » Reasoning: synthesizing multiple disparate findings is difficult
  • 9. ›› ExPlates ›› PivotLab ›› PurdueUniversity ››
  • 10. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Exploration Plates (ExPlates) » Data-flow method for visualization that automatically spatializes interaction 10 Spatialize…
  • 11. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Plate Anatomy » Building block: exploration plate – Visualization state: data, mapping, view – Input and output ports (anchors) – Connected by wires » Mutating ops create new plate(s) – Filtering, change visualization, transforms » Invariant ops update current plate – Color scale, viewport, formatting 11
  • 12. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Plate Types » Visualization plates: visual representations of input data » Data plates: data transformations from input to output » Annotation plates: add annotation to specific locations on the canvas 12
  • 13. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Output anchors Input anchors Control area Visualization area Data wires
  • 14. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Canvas and Layout » Infinitely zoomable visual canvas – Mouse control + automatic operations » Grid-based semi-automatic layout – Padding for data wires » Two ways to create new plates – Manual (menu) or automatic (spatializing) 14
  • 15. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› DEMO
  • 16. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› IMPLEMENTATION
  • 17. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Implementation » Web-based system (JavaScript + SVG) » Google Data Source API – Google Docs (spreadsheets) – RSS/Atom feeds – XML files – CSV files » Rendering: RaphaëlJS (raphaeljs.com) – Extensible with other SVG toolkits (D3, etc) 17
  • 18. ›› ExPlates ›› PivotLab ›› PurdueUniversity ››
  • 19. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› DISCUSSION
  • 20. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Discussion and Limitations » Scalability: complex exploration + size – Zooming and panning navigation – Web-based setting gives upper bound » Expertise: web-based but not intended for novice-level users » Comparison: relation to MDV tools – Data-flow (DataMeadow, GraphTrail) – Dashboard/workbench (Tableau, Spotfire) 20
  • 21. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› CONCLUSION
  • 22. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› » Spatializing exploration – Branching visual history – Duplicate, do not update » Data flow system – Automatic layout » Multidimensional data – Visualization + analysis » Web-based prototype – Live, dynamic updates
  • 23. ›› ExPlates ›› PivotLab ›› PurdueUniversity ›› Questions? Niklas Elmqvist Purdue University West Lafayette, IN, USA elm@purdue.edu »EuroVis2013»June17-21»LeipZig,Germany All images are Creative Commons from Flickr.com