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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Analysis and Aggregation

Supports multiple data mappings

Min, Max Weighted

Averages

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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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Fisheye view: Subset of details and overview

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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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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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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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Easy to see by color what happens before and after the target event24

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Event Operations Removing gaps Removing overlaps Combing different events

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Basketball analysisPhasesEvent sequencesEvents within events

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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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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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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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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.


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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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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Dual-scale

Network Diagram

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Dual-scale Tree

Map


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Different Visualization at each

scale


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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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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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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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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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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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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IEEE Vis Week 2013

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Transcript of IEEE Vis Week 2013