Interactive Latency in Big Data Visualization
Zhicheng "Leo" Liu, Research Scientist at the Creative Technologies Lab at Adobe Research
January 22nd, 2014
Reducing interactive latency is a central problem in visualizing large datasets. I discuss two inter-related projects in this problem space. First, I present the imMens system and show how we can achieve real-time interaction at 50 frames per second for billions of data points by combining techniques such as data tiling and parallel processing. Second, I discuss an ongoing user study that aims to understand the effect of interactive latency on human cognitive behavior in exploratory visual analysis.
Big Data Visualization Meetup - South Bay
http://www.meetup.com/Big-Data-Visualisation-South-Bay/
2. Latency:
a measure of time delay experienced in a system
rotational latency
network latency
query latency
interactive latency
3.
4. Questions
How to reduce interactive latency in big data visualization?
How does interactive latency affect user behavior?
5. Questions
How to reduce interactive latency in big data visualization?
How does interactive latency affect user behavior?
6. Reducing Latency
More memory
in-memory data store
Clever indexing
cube representation schemes
Parallel processing
multicore, GPGPU, distributed platforms
7. imMens: a holistic approach
Perceptual scalability
Binned aggregation as primary data reduction strategy
Interactive scalability
Multivariate data tiles
Parallel query processing and rendering on GPU
[Liu et. al. 2013]
8. imMens: a holistic approach
Perceptual scalability
Binned aggregation as primary data reduction strategy
Interactive scalability
Multivariate data tiles
Parallel query processing and rendering on GPU
[Liu et. al. 2013]
9. Guiding Principle
Perceptual & interactive scalability should be limited
by the chosen resolution of the visualized data,
not the number of records.
19. imMens: a holistic approach
Perceptual scalability
Binned aggregation as primary data reduction strategy
Interactive scalability
Multivariate data tiles
Parallel query processing and rendering on GPU
[Liu et. al. 2013]
47. Client-Side Processing
47
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768 769 … 1023
512
513
…
767
R
G
B
A
R
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B
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…
…
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R
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B
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data
Hles
query
fragment
shader
Y
[768-‐1023]
X
[512-‐767]
{
0
1
…
11
Pass
1
projecHons
off-‐screen
FBO
render
fragment
shader
Pass
2
canvas
Pack
data
Hles
as
images
(352KB
for
Brightkite)
Bind
to
WebGL
context
as
textures
48. 48
Simulate brush & linking across
plots in a scatter plot matrix
imMens vs. full data cube
60 synthesized datasets
Parameters
bin count per dimension
(10,20,30,40,50)
number of records
(10K, 100K, 1M, 10M, 100M, 1B)
number of dimensions (4,5)
Performance Benchmarks
49. 49
Google Chrome v.23.0.1271.95 on a quad-core 2.3 GHz MacBook Pro (OS X 10.8.2) with per-core 256K L2
caches, shared 6MB L3 cache and 8GB RAM. PCI Express NVIDIA GeForce GT 650M graphics card with
1024MB video RAM.
51.9
52.3
51.6
52.0
53.2
52.1
5.5
3.0
2.2
50. 50
Google Chrome v.23.0.1271.95 on a quad-core 2.3 GHz MacBook Pro (OS X 10.8.2) with per-core 256K L2
caches, shared 6MB L3 cache and 8GB RAM. PCI Express NVIDIA GeForce GT 650M graphics card with
1024MB video RAM.
51.9
52.3
51.6
52.0
53.2
52.1
5.5
3.0
2.2
51. 51
Google Chrome v.23.0.1271.95 on a quad-core 2.3 GHz MacBook Pro (OS X 10.8.2) with per-core 256K L2
caches, shared 6MB L3 cache and 8GB RAM. PCI Express NVIDIA GeForce GT 650M graphics card with
1024MB video RAM.
51.9
52.3
51.6
52.0
53.2
52.1
5.5
3.0
2.2
50fps querying and
rendering of 1B data points
56. Newell (1994) Card et al (1983) Example Time Range
deliberate act perceptual fusion recognize a pattern,
track animation
~100 milliseconds
cognitive operation unprepared response click a link,
select an object
~1 second
unit task unit task edit a line of text,
make a chess move
~10 seconds
69. What is an insight?
"many new airlines emerged around year 2003”
"HP started in 2001, AS in 2003, PI in 2004, OH in 2003”
“OH started in 2003, and they are doing pretty well
in terms of delays”
70. Questions
How to reduce interactive latency in big data visualization?
imMens: a system supporting real-time interaction
binned aggregation for perceptual scalability
multivariate data tiles & GPU processing for low latency
How does interactive latency affect user behavior?
Comparative study: quantitative & qualitative analysis
71. Questions
How to reduce interactive latency in big data visualization?
imMens: a system supporting real-time interaction
binned aggregation for perceptual scalability
multivariate data tiles & GPU processing for low latency
How does interactive latency affect user behavior?
72. Questions
How to reduce interactive latency in big data visualization?
imMens: a system supporting real-time interaction
binned aggregation for perceptual scalability
multivariate data tiles & GPU processing for low latency
How does interactive latency affect user behavior?
User study: quantitative & qualitative analysis
