[2017 데이터 진흥주간] 빅데이터 윈윈 컨퍼런스 데이터 시각화 자료_세종대 장윤교수

1. Data Visualization &
Big Data Visual Analytics
Yun Jang ( jangy@sejong.edu)
Data Visualization Lab (vis.sejong.ac.kr )
Sejong University

2. 2

3. Data Overload
Opportunity: Huge amount of data available in
digital form and ready for analysis!
• But, how can we make sense of this data?
• How can we harness this data in the decision making process?
• How do we avoid being overwhelmed by all of this data?
3

4. Data to Information
• We need to take all of this data and
transform it into information
• It’s how many petaflops of insights we can
generate from this data
• We need to make the data understandable
to people
4

5. 7 ± 2
Number of items an average human
holds in working memory
George Miller, 1956
5

6. 6
7
Data Mining Machine Learning
Artificial Intelligence
Data Visualization
Data Cleansing
Data wrangling

7. What is Visualization?
• “Computer-supported, interactive visual
representations of data to amplify cognition.”1
• This is not simply the process of making a
graphic or an image; the goal is to create
insight, not pretty pictures
7
1-Readings in Information Visualization: Using Vision to Think, SK Card, J Mackinlay and B.Shneiderman, 1999

8. Purpose of Visualization
• Analysis – Understand and explore your
data better
• Presentation – Communicate and inform
others more effectively
8

9. Visualization Community
9
Scientific
Visualization
Information
Visualization
Visual
Analytics

10. Scientific Visualization
• Something physical or geometrics
• The structure is typically defined or given
• Examples
– Air flow over a wing
– Organs in the human body
– Molecular bonding
10

11. Scientific Visualization
11
Illustrative Buffer Abstract space with Information Theory
Multimodal Rendering Functional Representation Quantum Chemistry Vis Illustrative Flow Vis

12. Information Visualization
• No direct physical correspondence
• Notion of the data is abstract
• Infographics & design of the abstraction
• Examples
– Baseball statistics
– Stock trends
– My social network
12

13. Information Visualization
13
Multivariate Abstraction Technique
for Geovisualization
Typographic
Maps
Communication Pattern Analysis
in the amusement park

14. Flow Map Extraction without Trajectory
14
(b)
t-1 t t+1
flow
(a)
t t+1
(c)
t-2 t-1 t t+1 t+2
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IEEE TVCG, to appear
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time
Spatiotemporal Statistical Data
Functional Representations
Flow maps
3D
Gravity model
Kernel
Density
Estimation
𝑡0 − 1 𝑡0 𝑡0 + 1
Indianapolis
Fort Wayne
Indianapolis
Fort Wayne
Indianapolis
Fort Wayne
Indianapolis
Fort Wayne

15. Key Tasks for Visualization
Search
Browse
Analyze
Monitor
15

16. Is This the Total Solution?
• Traditional visualization misses several key
factors in how people solve difficult
problems
• Visual analytics is invented to support the
decision making environment with an
interactive human-computer exploration
16

17. Visual Analytics Definition
Visual Analytics is the science of
analytical reasoning facilitated by
interactive visual interfaces1
17
1. Illuminating the Path: The R&D
Agenda for Visual Analytics,
Editors: Thomas and Cook

18. 18
D. A. Keim, F. Mansmann, A. Stoffel and H. Ziegler,
Visual Analytics.
Encyclopedia of Database Systems, Springer, 2009.

19. 19
Scientific
Visualization
Information
Visualization
Visual
Analytics
Statistical and
Information
Analytics Geospatial
Analytics
Scientific
Computing
Graphics
and
Visualization
Knowledge
DiscoverData
Management
& Knowledge
Decision
Science
Design and
Communication
Cognitive &
Perceptual
Science
Interactive
Science

20. 2020
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Dest. DestinationsDestinations
Origins
Carrier Delay
Weather Delay
National Aviation Delay
Carrier Delay, 14
Weather Delay, 2
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Security Delay, 0
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Air Traffic Delay Visual Analytics:
High-dimensional Multivariate Network Links

21. Public Safety Visual Analytics
21
Coast Guard Data VA
911 Data VA
Information Visualization 2013
IEEE Pacific Visualization 2014

22. Spatiotemporal Social Media Analytics
for Abnormal Event Detection
22IEEE VAST 2012

23. Public behavior response analysis in disaster
events utilizing visual analytics of microblog data
23EuroVA 2013, Computer and Graphics 2014
2012.10.14 2012.10.21 2012.10.28
Super
market
Park
Shelter
Hospital
School
Theater

24. Real-time Predictive Visual Analytics
in Text Data Streams
24JoV 2017

25. Conclusions
• Visualization is an important approach to
explore and analyze data
• Visual Analytics combines interactive visual
interfaces with algorithmic methods for data
pre-processing, transformation, and
feature/pattern extraction
• Visual Analytics is meant to be helping
people make sense from complex big data
25

26. Acknowledgment
• David Ebert, Ronny Peikert, Min Chen, Thomas Ertl, Kelly Gaither,
Niklas Elmqvist, Ross Maciejewski
• PhD students
– Hanbyul Yeon, Seokyeon Kim, Sangbong Yoo,
• Masters students
– Mingyu Pi, SeongMin Jeong, Daegyo Jeong
• Undergrads
– Dohyun Kim, Sujin Jeong, Seongbum Seo, Sunseo Hong, Hyunjin Kang,
Jaeseok Yoo
• All coauthors
26

27. 27
Thanks!!