Interactive Latency in Big Data Visualization

Zhicheng “Leo” Liu Jan 22, 2014

Latency: a measure of time delay experienced in a system

rotational latency

network latency

query latency

interactive latency

Questions

How to reduce interactive latency in big data visualization? How does interactive latency affect user behavior?

Questions

How to reduce interactive latency in big data visualization? How does interactive latency affect user behavior?

Reducing Latency

More memory in-memory data store

Clever indexing cube representation schemes

Parallel processing multicore, GPGPU, distributed platforms

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]

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]

Guiding Principle

Perceptual & interactive scalability should be limited by the chosen resolution of the visualized data,

not the number of records.

10  

Data

11  

Data

Alpha-blending

12  

Data

13  

Data Sampling

14  

Data Sampling

Modeling

15  

Data Sampling

Modeling Binned Aggregation

Google Fusion Tables: Sampling

16  Sampling

17  Aggregation

Binned Plots: Design Space

18  

numeric   ordinal/categorical   temporal   geographic  

1D  

2D  

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]

Demo

Multivariate Data Tiles

21

Projections / Materialized database views

Provide data for dynamic visualization

Much faster than a traditional data cube

22  

Brush & Link: A Naïve Approach

23  

X!

Y!

256

…

767

512 1023 …

Day!

Hour!

Month!

23 …

0 1 … 30

0 …

11

1

23 …

0 …

11

0 1 … 30 0 1 … 30

0

23 …

0

11

1

0

…

1

0

12 x 31 x 24 x 512 x 512 = ~2.3 billion cells

Brushing Over January

24  

X!

Y!

256

…

767

512 1023 …

Day!

Hour!

Month!

23 …

0 1 … 30

0 …

11

1

23 …

0 …

11

0 1 … 30 0 1 … 30

0

23 …

0

11

1

0

…

1

0

31 x 24 x 512 x 512 = ~195 million cells

Sum Along Day

25  

X!

Y!

256

…

767

512 1023 …

[ 0 – 30 ] Day!

Hour!

Month!

23 …

0 …

11

1

23 …

0 …

11

[ 0 – 30 ] [ 0 - 30 ]

0

23 …

0

11

1

0

…

1

0

24 x 512 x 512 = ~6 million cells

Sum Along Hour

26  

X!

Y!

256

…

767

512 1023 …

[ 0 – 30 ] Day!

Hour!

Month!

[ 0 – 23 ] 0

… 11

0 …

11

[ 0 – 30 ] [ 0 - 30 ]

[ 0 – 23 ] 0

11 …

[ 0 – 23 ]

512 x 512 cells

Decomposing a Data Cube

27  

For any pair of 1D or 2D binned plots, the maximum number of dimensions needed to support brushing & linking is 4.

full 5-D cube!

Day!

Hou

r!

Month!

0 1 … 30

0 …

11

Y!

Hou

r!X!

512 513 … 1023

256 … 767

Y!

Day!

X!

512 513 … 1023

256 … 767

Y!

Mon

th!

X!

512 513… 1023

256 … 767

3-D !cubes!

23 …

1 0

23 …

1 0

30 …

1 0

11 …

1 0

Σ   Σ   Σ   Σ  

28  

Tiles

29  

X: 256-511 X: 512-767

Y: 5

12-7

67

Y: 7

68-1

023

Day: 31 bins

Y:  512  -­‐  1023  

day:    0  -­‐  31  

From Datacube to Data Tiles

30  

512 513 … 767

256 …

511

30 …

1 0

512 513 … 767

512 …

767

30 …

1 0

768 769 … 1023

256 …

511

30 …

1 0

768 769 … 1023

512 …

767

30 …

1 0

Data Tiles

31  

x1-y1-month

32  

x1-y1-day

33  

x1-y1-hour

34  

x1-y2-month

35  

x1-y2-day

36  

x1-y2-hour

37  

x2-y1-month

38  

x2-y1-day

39  

x2-y1-hour

40  

x2-y2-month

41  

x2-y2-day

42  

x2-y2-hour

43  

month-day-hour

44  

45  

imMens Architecture

46  SciDB,  Postgres  

Client  

Server  

UI  control   VisualizaHon  

specify  

brush    &  link  

zoom  &  pan  

Client-Side Processing

47  

0

1

… 11

768 769 … 1023

512 513

… 767

R   G   B   A  

R   G   B   A  

…   …   …   …  

R   G   B   A  

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  

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  

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  

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  

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

Speed of Thought?

Questions

How to reduce interactive latency in big data visualization? How does interactive latency affect user behavior?

Newell (1994): Unified Theories of Cognition

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

~300ms: The Embodiment Level

Deictic Strategy

Pointing movements bind objects in the world

Small changes in cost of binding cause different cognitive behavior

Latency affects high-level/longitudinal strategies

Block-copying Ballard et al (1995, 1997)

8-puzzle solving O’Hara and Payne (1998, 1999)

Search Brutlag (2009)

Exploratory Visual Analysis?

Operation Low High brush & link ~20ms ~20ms + 500ms

select ~20ms ~20ms + 500ms

pan ~100ms ~100ms + 500ms

zoom ~1000ms ~1000ms + 500ms

Latency Conditions

Datasets

Study Design

16 participants, 32 observations 2 X 2 between subject interaction logs audio transcripts

Log Events

System and Mouse Events brush, select, zoom, pan, clear, color slider, log scale tiles cached, mouse down, mouse up, mouse move

Trigger vs. Processed System Events debouncing keeps system usable timestamp, event type, parameters

Normalized Processed Events

How to Evaluate Performance?

The purpose of visualization is insight, not pictures.

Counting Insights

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”

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

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?

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

Acknowledgment

Jeffrey Heer Biye Jiang

Thank You