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Transcript of IEEE Vis Week 2013
IEEE VIS WEEK 2013
David Sheets
AGENDA
Conference Format Areas of Research Common Themes Paper Details
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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!3
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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
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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
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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
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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
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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.
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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?”
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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.
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PAPER DETAILS
LineUp TimeBench Event Simplification Traffic Jam Analysis Spatial Clustering Common Angle Plots Hybrid Visualizations Nanocubes
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3InfoVis
LINEUP: MULTI-ATTRIBUTE RANKINGS
Expands on the common tabular view Place visualization instead of number
Supports aggregation of columns
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LINEUP: MULTI-ATTRIBUTE RANKINGS
Analysis and Aggregation
Supports multiple data mappings
Min, Max Weighted
Averages
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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 15
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LINEUP: MULTI-ATTRIBUTE RANKINGS
Fisheye view: Subset of details and overview
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LINEUP: MULTI-ATTRIBUTE RANKINGS
Questions?
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* Visualizations are from other efforts using TimeBench and not part of TimeBench
VAST
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
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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
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TIMEBENCH:LIBRARY FOR TIME-ORIENTED DATA
Questions?
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EVENT SIMPLIFICATION
Alignment of data around event Support analysis of what happens prior to the
event And what happens after
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EVENT SIMPLIFICATION
Easy to see by color what happens before and after the target event24
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EVENT SIMPLIFICATION
Event Operations Removing gaps Removing overlaps Combing different events
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EVENT SIMPLIFICATION
Basketball analysisPhasesEvent sequencesEvents within events
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EVENT SIMPLIFICATION
Questions?
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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.
VAST
TRAFFIC JAM ANALYSIS
Claimed Contributions Process to extract traffic jams Visual interface to explore traffic jam
propagation
Extraction Process1. Road Network Processing2. GPS Data Cleaning3. Map Matching4. Road Speed Calculation5. Traffic Jam Detection6. Propagation Graph Construction 29
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TRAFFIC JAM ANALYSIS
1. Road Network Processing Extracting the road data from a source Used OpenStreetMap jXAPI http://wiki.openstreetmap.org/wiki/Xapi
2. GPS Data Cleaning Remove unrealistic points (too fast, outside area,
etc.) Remove duplicate points Remove stops immediately after drop-off
3. Map Matching ST-matching but…
Speed limits aren’t available so only S-matching Road data has many errors so allow unmatched results
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TRAFFIC JAM ANALYSIS
4. 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
5. 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
6. Propagation Graph Construction e1.t0 ≤ e2.t0 ≤ e1.t1 e1.d is immediately ahead of e2.d 31
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TRAFFIC JAM ANALYSIS
TRAFFIC JAM ANALYSIS
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a) In Beijing, different roads have different traffic patterns.
b) The main road in the North 3rd Ring is regularly congested at weekdays in the morning and afternoon.
c) 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.
d) 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.
e) Same as (d)
f) The road besides the new National Exhibition Center at Shunyi is congested when there are exhibitions.
g) The Airport Express is occasionally congested by unpredictable incidents.
h) The road to the east of Beijing Worker’s Stadium is regularly congested at the night of Friday and Saturday.
TRAFFIC JAM ANALYSIS
Questions?
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3InfoVis
COMMON ANGLE PLOTS
Which group had more survivors?
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COMMON ANGLE PLOTS
Find areas where the difference is high Lower line is exports to East Indies Upper line is imports from East Indies
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COMMON ANGLE PLOTS
Visualizations are about perception Visualization encodes value on horizontal
width People tend to relate to the orthogonal
distance
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COMMON ANGLE PLOTS
Comparison
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COMMON ANGLE PLOTS
Questions?
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3InfoVis
HYBRID-IMAGE VISUALIZATION
Take advantage of perception at distance Perception changes based on distance Contributions
Methods to take advantage of wall-sized displays
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HYBRID-IMAGE VISUALIZATION
Blending Process1. Near image is high-pass filtered.2. Far image, is low-pass filtered.3. After filtering, the two images are alpha-
blended.
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HYBRID-IMAGE VISUALIZATION
Dual-scale
Network Diagram
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HYBRID-IMAGE VISUALIZATION
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Dual-scale Tree
Map
HYBRID-IMAGE VISUALIZATION
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Different Visualization at each
scale
HYBRID-IMAGE VISUALIZATION
Questions?
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3InfoVis
NANOCUBES FOR SPATIOTEMPORAL DATA
Equated to Data Cubes Aggregate data for faster queries Operations
GROUP_BY CUBE ROLL_UP
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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
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NANOCUBES FOR SPATIOTEMPORAL DATA
But where is temporal? Nanocubes order data by:
1. Spatial2. Categorical3. Temporal
The visualizations arestructured the same…(also recall first slide)
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NANOCUBES FOR SPATIOTEMPORAL DATA
But seriously, where is temporal? Uses a technique called summed-area tables Each node in the tree stores one of these
tables Tables contain each time stamp as
cumulative count Two binary searches to get count
Min lower Max upper Max–Min=Answer
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NANOCUBES FOR SPATIOTEMPORAL DATA
Contributions Data structure
enables real-time queries
Support interactive visualizations
Limitations High-memory use,
currently all in RAM Queries only provide
aggregate results vs. individual records
Only one spatial and one temporal dimension e.g. Flight takes off
from one location and lands in a second
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NANOCUBES FOR SPATIOTEMPORAL DATA
Questions?
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QUESTIONS?
Final questions and general discussion
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BACK UP SLIDES
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HYPE CYCLE
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Mimicking Analysis Validation
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Zoom In