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IEEE Vis Week 2013

IEEE Vis Week 2013. David Sheets. Agenda. Conference Format Areas of Research Common Themes Paper Details. Conference Format. Fast-Forward All sub-conferences simultaneous sessions About five papers presented at each session Two morning sessions Two or three afternoon sessions Panels

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IEEE Vis Week 2013

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  1. IEEE Vis Week 2013 David Sheets

  2. Agenda • Conference Format • Areas of Research • Common Themes • Paper Details IEEE Vis Week 2013

  3. Conference Format • Fast-Forward • All sub-conferences simultaneous sessions • About five papers presented at each session • Two morning sessions • Two or three afternoon sessions • Panels • About five experts from industry and academics • Each presents their view of a topic • Longer question and answer period than for papers • Evening activities • Poster Session! IEEE Vis Week 2013

  4. Areas of Research • InfoVis – Information Visualization • Study of interactive visual representations • Focus is to leverage human cognition • Part new visualizations, part improving existing • SciVis – Scientific Visualization • Study of visualizations of real-world phenomena • Focus on accurate modeling with 3D rendering • Common for medical and geological data • VAST - Visual Analytics Science & Technology • Study of analytical reasoning supported by visualization • Combines Data Mining with Information Visualization • More consideration given to complete applications IEEE Vis Week 2013

  5. Areas of Research • InfoVis • Ordinal & Categorical Data • Perception & Cognition • Defining the Design Space • Storytelling & Presentation • Systems & Sets • Application Areas • Time, Trees & Graphs • High-Dimensional Data IEEE Vis Week 2013

  6. Areas of Research • SciVis • Volume and Surface Modeling • Uncertainty and Multivariate Analysis • Vector and Flow Visualization • Navigation, Interaction, and Evaluation • Biomedical Visualization • Visualization Systems • Volume Rendering IEEE Vis Week 2013

  7. Areas of Research • VAST • Modeling and Decision-Making • Text and Social Media • High-Dimensional Data • Images and Video • Space and Movement • Sensemaking and Collaboration • Temporal Analytics IEEE Vis Week 2013

  8. Common Themes • Big Data – So big it’s everywhere • Big Data isn’t new, it’s just more common • Big Data is a simple term designed to be memorable • Data too big for traditional analysis techniques • Panel: Successful Visualization & Big Data • Tie all the data together through meaningful aggregation • Provide an infrastructure and service to a community • Quick turn-around in providing results to decision makers • Allow customer (end-user) customizations • Combine search, analytics, and visualization in one tool • Integrate with ‘the cloud’ • Be highly accessible to the users • Facilitate the transfer of big data into big value. IEEE Vis Week 2013

  9. Common Themes • Visualizations & Big Data • Visualizing the end-result • But also helping to attain the end-result • Scalability • One solution to big data • Also supports interactivity • Commonly asked: “How does your solution scale?” IEEE Vis Week 2013

  10. Common Themes • Visualizations – It’s about the user • Evolution of research (hype-cycle): • Mimicking, Analysis, Validation • Originally about creating different techniques • Field now moving to validation – Involves users • Past, Present, and Future • Visualization is relatively new research area… … but it’s been around forever. IEEE Vis Week 2013

  11. Paper Details • LineUp • TimeBench • Event Simplification • Traffic Jam Analysis • Spatial Clustering • Common Angle Plots • Hybrid Visualizations • Nanocubes IEEE Vis Week 2013

  12. InfoVis IEEE Vis Week 2013

  13. LineUp: Multi-Attribute Rankings • Expands on the common tabular view • Place visualization instead of number • Supports aggregation of columns IEEE Vis Week 2013

  14. LineUp: Multi-Attribute Rankings • Analysis and Aggregation • Supports multiple data mappings • Min, Max • Weighted Averages IEEE Vis Week 2013

  15. LineUp: Multi-Attribute Rankings • Visualization of the trend. e.g. • How a ranking changes over time • How a ranking changes with different formulae • Can drag column width to manipulate weighting IEEE Vis Week 2013

  16. LineUp: Multi-Attribute Rankings • Fisheye view: Subset of details and overview IEEE Vis Week 2013

  17. LineUp: Multi-Attribute Rankings • Questions? IEEE Vis Week 2013

  18. VAST IEEE Vis Week 2013 * Visualizations are from other efforts using TimeBench and not part of TimeBench

  19. TimeBench:Library for Time-Oriented Data • Intended to abstract time-oriented operations • Attempts to formalize several works into code • Granularities – Units by which time is divided • Time Primitives • Instant – Single anchored point in time • Intervals – Defined by two instants and is also anchored • Spans – Unanchored duration between intervals • Determinacy • Amount of uncertainty • Combining data recorded at two different granularities IEEE Vis Week 2013

  20. TimeBench:Library for Time-Oriented Data • Other desiderata • Expressiveness • Tap into the complexity of time-oriented data • Offer primitives and granularities in a flexible manner • Common Data Structure • Allow reuse of integrated visualizations • Automated methods in a “polylithic fashion” • Developer Accessibility • Abstract complexities of time-oriented data • Still, speak the language of the time domain • Runtime Efficiency • Support interactive visualizations IEEE Vis Week 2013

  21. TimeBench:Library for Time-Oriented Data • Questions? IEEE Vis Week 2013

  22. VAST IEEE Vis Week 2013

  23. Event Simplification • Alignment of data around event • Support analysis of what happens prior to the event • And what happens after IEEE Vis Week 2013

  24. Event Simplification Easy to see by color what happens before and after the target event IEEE Vis Week 2013

  25. Event Simplification • Event Operations • Removing gaps • Removing overlaps • Combing different events IEEE Vis Week 2013

  26. Event Simplification Basketball analysis • Phases • Event sequences • Events within events IEEE Vis Week 2013

  27. Event Simplification • Questions? IEEE Vis Week 2013

  28. VAST IEEE Vis Week 2013 Fig. 1. An overview of our system. (a) The spatial view shows the traffic jam density on each road of Beijing by color, and one traffic jam propagation graph is highlighted in black. (b) The embedded road speed views show the speed patterns of four roads in the highlighted black propagation graph. (c) The graph list view shows a list of sorted traffic jam propagation graphs. (d) The multi-faceted filter view allows filtering of propagation graphs by time and size. (e) The graph projection view shows the topological relationship of graph clusters, where graphs in the same cluster have very similar topology.

  29. Traffic Jam Analysis • Claimed Contributions • Process to extract traffic jams • Visual interface to explore traffic jam propagation • Extraction Process • Road Network Processing • GPS Data Cleaning • Map Matching • Road Speed Calculation • Traffic Jam Detection • Propagation Graph Construction IEEE Vis Week 2013

  30. Traffic Jam Analysis • Road Network Processing • Extracting the road data from a source • Used OpenStreetMap jXAPI • http://wiki.openstreetmap.org/wiki/Xapi • GPS Data Cleaning • Remove unrealistic points (too fast, outside area, etc.) • Remove duplicate points • Remove stops immediately after drop-off • Map Matching • ST-matching but… • Speed limits aren’t available so only S-matching • Road data has many errors so allow unmatched results IEEE Vis Week 2013

  31. Traffic Jam Analysis • Road Speed Calculation • Average speed of matched trajectories on a segment • Average calculated per trajectory vs. per point • Use a support factor to determine if speed is valid • Traffic Jam Detection • Sort all speeds, take the speed at F% of the data • Use C% to designate traffic jam • Paper uses F=85 and C=45 • Propagation Graph Construction • e1.t0 ≤ e2.t0 ≤ e1.t1 • e1.d is immediately ahead of e2.d IEEE Vis Week 2013

  32. Traffic Jam Analysis IEEE Vis Week 2013

  33. Traffic Jam Analysis • In Beijing, different roads have different traffic patterns. • The main road in the North 3rd Ring is regularly congested at weekdays in the morning and afternoon. • This road is beside two primary schools, it is also congested at weekdays, but usually before 7:30am, when parents send their children to school. • The two directions of the tunnel just outside Beijing West Station congest at different times, one only in the morning, one only in the afternoon. • Same as (d) • The road besides the new National Exhibition Center at Shunyi is congested when there are exhibitions. • The Airport Express is occasionally congested by unpredictable incidents. • The road to the east of Beijing Worker’s Stadium is regularly congested at the night of Friday and Saturday. IEEE Vis Week 2013

  34. Traffic Jam Analysis • Questions? IEEE Vis Week 2013

  35. InfoVis IEEE Vis Week 2013

  36. Common Angle Plots • Which group had more survivors? IEEE Vis Week 2013

  37. Common Angle Plots • Find areas where the difference is high • Lower line is exports to East Indies • Upper line is imports from East Indies IEEE Vis Week 2013

  38. Common Angle Plots • Visualizations are about perception • Visualization encodes value on horizontal width • People tend to relate to the orthogonal distance IEEE Vis Week 2013

  39. Common Angle Plots • Comparison IEEE Vis Week 2013

  40. Common Angle Plots • Questions? IEEE Vis Week 2013

  41. InfoVis IEEE Vis Week 2013

  42. Hybrid-Image Visualization • Take advantage of perception at distance • Perception changes based on distance • Contributions • Methods to take advantage of wall-sized displays IEEE Vis Week 2013

  43. Hybrid-Image Visualization • Blending Process • Near image is high-pass filtered. • Far image, is low-pass filtered. • After filtering, the two images are alpha-blended. IEEE Vis Week 2013

  44. Hybrid-Image Visualization Dual-scale Network Diagram IEEE Vis Week 2013

  45. Hybrid-Image Visualization Dual-scale Tree Map IEEE Vis Week 2013

  46. Hybrid-Image Visualization Different Visualization at each scale IEEE Vis Week 2013

  47. Hybrid-Image Visualization • Questions? IEEE Vis Week 2013

  48. InfoVis IEEE Vis Week 2013

  49. Nanocubes for Spatiotemporal Data • Equated to Data Cubes • Aggregate data for faster queries • Operations • GROUP_BY • CUBE • ROLL_UP IEEE Vis Week 2013

  50. Nanocubes for Spatiotemporal Data • Spatial structure • Divided into partitions • That are divided into partitions • Each level branches according to category values • Each level can share values • Each level contains an All aggregate IEEE Vis Week 2013

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