Information Visualization at UBC
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Information Visualization at UBC
Tamara Munzner
University of British Columbia
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Information Visualization
• visual representation of abstract data– computer-based– interactive – goal of helping human perform some task more
effectively
• bridging many fields – cognitive psych: finding appropriate representation– HCI: using task to guide design and evaluation – graphics: interacting in realtime
• external representation reduces load on working memory
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Current Projects
• accordion drawing– TreeJuxtaposer, SequenceJuxtaposer,
TJC, PRISAD, PowerSetViewer
• evaluation– Focus+Context, Transformations
• graph drawing– TopoLayout
• dimensionality reduction– MDSteer, PBSteer
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Accordion Drawing
• rubber-sheet navigation– stretch out part of surface,
the rest squishes– borders nailed down– Focus+Context technique
• integrated overview, details
– old idea• [Sarkar et al 93], ...
• guaranteed visibility– marks always visible– important for scalability – new idea
• [Munzner et al 03]
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Guaranteed Visibility
• easy with small datasets
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Guaranteed Visibility Challenges
• hard with larger datasets
• reasons a mark could be invisible– outside the window
• AD solution: constrained navigation
– underneath other marks• AD solution: avoid 3D
– smaller than a pixel• AD solution: smart culling
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Guaranteed Visibility: Culling
• naive culling may not draw all marked items
GV no GV
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Phylogenetic/Evolutionary Tree
M Meegaskumbura et al., Science 298:379 (2002)
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Common Dataset Size Today
M Meegaskumbura et al., Science 298:379 (2002)
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Future Goal: 10M Node Tree of Life
David Hillis, Science 300:1687 (2003)
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Paper Comparison: Multiple Trees
focus
context
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TreeJuxtaposer
• comparison of evolutionary trees – side by side
• [demo: olduvai.sourceforge.net/tj]
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TJ Contributions
• first interactive tree comparison system– automatic structural difference computation– guaranteed visibility of marked areas
• scalable to large datasets– 250,000 to 500,000 total nodes– all preprocessing subquadratic– all realtime rendering sublinear
• introduced accordion drawing (AD)
• introduced guaranteed visibility (GV)
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Joint Work: TJ Credits• Tamara Munzner (UBC prof)• Francois Guimbretiere (Maryland prof)• Serdar Tasiran (Koc Univ, prof)• Li Zhang, Yunhong Zhou (HP Labs)
– TreeJuxtaposer: Scalable Tree Comparison using Focus+Context with Guaranteed Visibility
– Proc. SIGGRAPH 2003– www.cs.ubc.ca/~tmm/papers/tj
• James Slack (UBC PhD)• Tamara Munzner (UBC prof)• Francois Guimbretiere (Maryland prof)
– TreeJuxtaposer: InfoVis03 Contest Entry. (Overall Winner)– InfoVis 2003 Contest – www.cs.ubc.ca/~tmm/papers/contest03
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Genomic Sequences
• multiple aligned sequences of DNA
• now commonly browsed with web apps– zoom and pan with abrupt jumps
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SequenceJuxtaposer
• dense grid, following conventions – rows of sequences, typically species– columns of partially aligned nucleotides– [video: www.cs.ubc.ca/~tmm/papers/sj]
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SJ Contributions
• accordion drawing for gene sequences– smooth, fluid transitions between states– guaranteed visibility for globally visible
landmarks– difference thresholds changeable on the fly
• 2004 paper results: 1.7M nucleotides– current with PRISAD: 40M nucleotides
• future work– hierarchical structure from annotation dbs– editing
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Joint Work: SJ Credits
• James Slack (UBC PhD)
• Kristian Hildebrand (Weimar Univ MS)
• Tamara Munzner (UBC prof)
• Katherine St. John (CUNY prof)
– SequenceJuxtaposer: Fluid Navigation For Large-Scale Sequence Comparison In Context
– Proc. German Conference Bioinformatics 2004– www.cs.ubc.ca/~tmm/papers/sj
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Scaling Up Trees
• TJ limits: 500K nodes– large memory footprint– CPU-bound, far from achieving peak
rendering performance of graphics card
• in TJ, quadtree data structure used for– placing nodes during layout– drawing edges given navigation– culling edges with GV– picking edges during interaction
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New Data Structures, Algorithms
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• new data structures– two 1D hierarchies vs. one 2D quadtree
• new drawing/culling algorithm
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TJC/TJC-Q Results
• TJC– no quadtree– picking with new hardware feature
• requires HW multiple render target support
– 15M nodes
• TJC-Q– lightweight quadtree for picking support– 5M nodes
• both support tree browsing only– no comparison data structures
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Joint Work: TJC, TJC-Q Credits
• Dale Beermann (Virginia MS alum)
• Tamara Munzner (UBC prof)
• Greg Humphreys (Virginia prof)
– Scalable, Robust Visualization of Large Trees – Proc. EuroVis 2005– www.cs.virginia.edu/~gfx/pubs/TJC
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PRISAD
• generic accordion drawing infrastructure– handles many application types
• efficient– guarantees of correctness: no overculling– tight bounds on overdrawing
• handles dense regions efficiently
– new algorithms for rendering, culling, picking• exploit application dataset characteristics instead
of requiring expensive additional data structures
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PRISAD Results
• trees– 4M nodes– 5x faster rendering, 5x less memory– order of magnitude faster for marking
• sequences– 40M nucleotides
• power sets– 2M to 7M sets– alphabets beyond 20,000
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Joint Work: PRISAD Credits
• James Slack (UBC PhD)
• Kristian Hildebrand (Weimar MS)
• Tamara Munzner (UBC prof)
– PRISAD: A Partitioned Rendering Infrastructure for Scalable Accordion Drawing.
– Proc. InfoVis 2005, to appear
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PowerSetViewer
• data mining of market-basket transactions– show progress of steerable data mining system
with constraints– want visualization “windshield” to guide
parameter setting choices on the fly
• dynamic data– all other AD applications had static data
• transactions as sets– items bought together make a set– alphabet is items in stock at store– space of all possible sets is power set
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PowerSetViewer
• show position of logged sets within enumeration of power set– very long 1D linear list– wrap around into 2D grid of fixed width– [video]
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Joint Work: PSV Credits
• work in progress
• Tamara Munzner (UBC prof)
• Qiang Kong (UBC MS)
• Raymond Ng (UBC prof)
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Current Projects
• accordion drawing– TreeJuxtaposer, SequenceJuxtaposer,
TJC, PRISAD, PowerSetViewer
• Focus+Context evaluation– system, perception
• graph drawing– TopoLayout
• dimensionality reduction– MDSteer, PBSteer
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Focus+Context
• integrating details and overview into single view– carefully chosen nonlinear distortion– what are costs? what are benefits?
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Focus+Context System Evaluation
• how focus and context are used with– rubber sheet navigation vs. pan and zoom– integrated scene vs. separate overview
• user studies using modified TJ– abstract tasks derived from biologists’
needs based on interviews
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Joint Work: F+C System Eval Credits
• work in progress
• Adam Bodnar (UBC MS)
• Dmitry Nekrasovski (UBC MS)
• Tamara Munzner (UBC prof)
• Joanna McGrenere (UBC prof)
• Francois Guimbretiere (Maryland prof)
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F+C Perception Evaluation
• understand perceptual costs of transformation– find best transformation to use
• visual search for target amidst distractors – shaker paradigm
static 1 (original)
static 2 (transformed)
Averageperformanceon staticconditions
Performanceon alternatingcondition
vs.
variable alternation rate
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F+C Perception Evaluation
• understand perceptual costs of transformation– deterioration in performance
• time, effort, error
– static costs: caused by crowding, distortion of static transformation itself
• high static cost
– dynamic costs: reorienting and remapping when transformation applied or focus moved
• low dynamic cost• large no-cost zone
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Joint Work: F+C Perceptual Eval
• Keith Lau (former UBC undergrad)• Ron Rensink (UBC prof)• Tamara Munzner (UBC prof)
– Perceptual Invariance of Nonlinear Focus+Context Transformations
– Proc. First Symposium on Applied Perception in Graphics and Visualization, 2004
• work in progress: continue investigation• Heidi Lam (UBC PhD)• Ron Rensink (UBC prof)• Tamara Munzner (UBC prof)
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Current Projects
• accordion drawing– TreeJuxtaposer, SequenceJuxtaposer,
TJC, PRISAD, PowerSetViewer
• Focus+Context evaluation– system, perception
• graph drawing– TopoLayout
• dimensionality reduction– MDSteer, PBSteer
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TopoLayout
• multilevel decomposition and layout– automatic detection of topological features
• chop into hierarchy of manageable pieces– lay out using feature-appropriate algorithms
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Multilevel Hierarchies
• strengths: handles large class of graphs– previous work mostly good with near-meshes
• weaknesses: poor if no detectable features
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Joint Work: TopoLayout Credits
• work in progress
• Dan Archambault (UBC PhD)
• Tamara Munzner (UBC prof)
• David Auber (Bordeaux prof)
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Current Projects
• accordion drawing– TreeJuxtaposer, SequenceJuxtaposer,
TJC, PRISAD, PowerSetViewer
• Focus+Context evaluation– system, perception
• graph drawing– TopoLayout
• dimensionality reduction– MDSteer, PBSteer
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Dimensionality Reduction
• mapping multidimensional space into space of fewer dimensions– typically 2D for infovis– keep/explain as much variance as possible– show underlying dataset structure
• multidimensional scaling (MDS)– minimize differences between interpoint
distances in high and low dimensions
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Scalability Limitations
• high cardinality and high dimensionality: slow– motivating dataset: 120K points, 300 dimensions– most existing software could not handle at all– 2 hours to compute with O(n5/4) HIVE [Ross 03]
• real-world need: exploring huge datasets– people want tools for millions of points
• strategy– start interactive exploration immediately
• progressive layout
– concentrate computational resources in interesting areas• steerability
– often partial layout is adequate for task
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b
lay out random subset
subdivide bins
lay out another random subset
user selects active region of
interest
more subdivisions and layouts
user refines active region
MDSteer Overview
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MDSteer Contributions• first steerable MDS algorithm
– progressive layout allows immediate exploration– allocate computational resources in lowD space– [video: www.cs.ubc.ca/~tmm/papers/mdsteer]
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Joint Work: MDSteer Credits
• Matt Williams (former UBC MS)• Tamara Munzner (UBC prof)
– Steerable Progressive Multidimensional Scaling– Proc. InfoVis 2004– www.cs.ubc.ca/~tmm/papers/mdsteer
• work in progress: PBSteer for progressive binning– David Westrom (former UBC undergrad)– Tamara Munzner (UBC prof)– Melanie Tory (UBC postdoc)
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Summary
• broad array of infovis projects at UBC
• theme: scalability– size of dataset– number of available pixels
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InfoVis Service
• IEEE Symposium on Information Visualization (InfoVis) Papers/Program Co-Chair 2003, 2004
• IEEE Executive Committee, Technical Committee on Visualization and Graphics
• Visualization Research Challenges– report commissioned by NSF/NIH
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More Information
• papers, videos, images– www.cs.ubc.ca/~tmm
• free software– olduvai.sourceforge.net/tj– olduvai.sourceforge.net/sj