1. Big Data Analytics 大数据的可视化 Huamin Qu Hong Kong University of Science and Technology

2. What is data visualization? “Data visualization is the creation and study of the visual representation of data” - wiki Input: data Output: visual form Goal: insight

3. Why visualization?

4. Anscombe’s Quartet: Four datasets

5. Anscombe’s Quartet: Statistics

6. Anscombe’s Quartet: Visualizations

7. Bible

8. GapMinder

9. Wealth Inequality in America https://www.youtube.com/watch?v=QPKKQnij nsM https://www.youtube.com/watch?v=QPKKQnij nsM

10. Data Visualization Vis is hot & cool Vis is young What is vis research? What are we doing at HKUST?

11. WSJ

18. New York Times The New York Times Visualization Lab, which allows readers to create compelling interactive charts, graphs, maps and other types of graphical presentations from data made available by Times editors. … The Visualization Lab is based on technology from I.B.M. Research called Many Eyes

20. Obama’s big data plan A $2 million award for a research training group in big data that will support training for undergraduate and graduate students and postdoctoral fellows using novel statistical, graphical, and visualization techniques to study complex data.

21. Obama’s big data plans The Department of Energy is getting in on the big data frenzy, too, investing $25 million to develop Scalable Data Management, Analysis and Visualization Institute, which aims to develop techniques for visualizing the incredible amounts of data generated by the department’s team of supercomputers.

22. Tableau

26. Data Visualization Vis is hot & cool Vis is young What is vis research? What are we doing at HKUST?

27. Subfields Scientific Visualization (SciVis) – Spatial data Information Visualization (InfoVis)– Abstract data Visual Analytics (VAST) – Analytical reasoning

28. VAST (Visual Analytics Science and Technology) InfoVis (Information Visualization) SciVis (Scientific Visualization) VIS Conferences

29. VIS - Infographics Infographics is static Visualization is interactive

30. StoryLine

33. VIS - HCI Visualization deals with data HCI deals with everything involving human & computer interaction

34. VIS - Data Mining Data mining focuses more on automatic algorithms Visualization keeps human in the loop and focuses more on interactive analysis

35. VA and Data Mining Data Analysis – let computers do what computers are good at Data mining – let humans do what they're good at Visual analytics

36. Data mining Visual Analytics Human Computer Interaction Computer Graphics Daniel A. Keim’s perspective on visual analytics

38. Data Visualization Vis is hot & cool Vis is young What is vis research? What are we doing at HKUST?

39. What is vis research? Techniques/algorithms Applications Systems Evaluations Theory/models

40. Visualization Process Visualization Pipeline Engineering part Visual design part

41. Technique Papers Novel techniques or algorithms

42. Application/Design Study Papers Applying visualization and visual analytics techniques in an application area.

43. Challenges for Visualization Research Scalability

44. Better Scalability -Better visualization -More people

45. Better Visualization Better overview & summarization Better data reduction Better visual encoding Better user interaction ….

46. Top Ten Interaction Challenges in Extreme-Scale Visual Analytics P.C. Wong, H.W. Shen and C. Chen 2012 1.In Situ Interactive Analysis 2.User-Driven Data Reduction 3.Scalability and Multi-level Hierarchy 4.Representation of Evidence and Uncertainty 5.Heterogeneous Data Fusion

47. Top Ten Interaction Challenges in Extreme-Scale Visual Analytics 6. Data Summarization and Triage for Interactive Query 7. Analytics of Temporally Evolving Features 8.The Human Bottleneck 9.Design and Engineering Development 10. The Renaissance of Conventional Wisdom

48. How to Get More People? Lower the bar for visualizations Bring visualizations to masses

49. Lower the Bar for Visualizations Google Visualization API IBM’s RAVE and Many Eyes D3.js Taobao’s datav.org

50. D3.js

51. IBM Many Eyes

52. Taobao

55. Data Visualization Vis is hot & cool Vis is young What is vis research? What are we doing at HKUST?

56. Visualization@HKUST

57. Medical Data Visualization TVCG’07 TVCG’08 (Vis’08) TVCG’09 1 (Vis’09) TVCG’09 2 (Vis’09) TVCG 2011

58. High Dimensional and Relational Data TVCG’08 (InfoVis’08) CGF’08 (EuroVis’08) CGF’09 (EuroVis’09) TVCG’09 (InfoVis’09) TVCG’11 InfoVis’11

59. Text Data Dynamic Tag Clouds IEEE CG&A’ 11 OpinionSeer TVCG’10 (InfoVis’10) FacetAtlas TVCG’10 (InfoVis’10) TextFlow TVCG’11 (InfoVis’11) TextWheel ACM TIST’12

60. Urban Informatics Air pollution analysis TVCG’07 (Vis’07) Route Zooming TVCG’09 (Vis’09) Trajectory Analysis VAST’11 Mobile Phone Data

61. Social Flow

62. Whisper When and where is a story dispersed?

63. How information is spread ? Query earthquake on Twitter

64. Information diffusion: When, where and how the ideas are spread ? The temporal trend, social-spatial extent and community response to a topic of interest.

65. Challenges How can we represent the dynamic information diffusion processes for diffusion pattern detection and comparison? – How can we deal with a huge amount of micro blog data in real time? – How can we summarize the diffusion processes by considering temporal, spatial, topic and community information?

66. Solution – Whisper System Goal: visualize the information diffusion process in terms of the temporal trend, social- spatial extent, and community response to a topic of interest

67. Design Goals While tracing information diffusion in micro- blogs, it would be crucial to pick out and summarize the information spread by opinion leaders (B). Visualization about a process should both have the dynamic updating feature supporting realtime monitoring and have the temporal exploration feature supporting backward scrutinization of historical patterns (D).

68. Visualization design Color hues: sentiments Color opacity: activeness of tweets / users Size: importance (number of followers) Shape: types (media / common users / retweeting activity)

69. Visualization design Diffusion series – When mouse over on an active tweet, its diffusion series will be shown to illustrate ``when did who reweet whom”.

71. Layout Topic Disc Diffusion Pathway User Group Assembling

72. Layout Topic Disc Diffusion Pathway User Group Assembling Sunflower Packing

73. Layout Topic Disc Diffusion Pathway User Group Assembling Diffusion Field – Based on electronic fields – Topic positive charged – User group negative charged

74. Layout Topic Disc Diffusion Pathway User Group Assembling Flux line layout

75. Layout Topic Disc Diffusion Pathway User Group Assembling Users are grouped either by geo-locations / their shared interests – Use voronoi icons to facilitate comparison – Use sunflower packing for efficient layout in case of online monitoring Layout by longitude or evenly separate circle space

76. Layout Topic Disc Diffusion Pathway User Group Assembling Visual clutter reduction – Reorder the user groups in match with the center active tweets to reduce crossings – Rotate the topic disc to minimize the line lengths

77. Stories (1) : tracing diffusion events Japan Earthquake

78. Temporal diffusion pattern (Opinion Leaders)

79. Interview with Domain Experts Three experts: – political scientist, design expert, communication phD Interview methodology: – warm up; semi-structure interview with 7 questions; free discussions Results Discussions

80. A Complete VA System Design goals Underlying techniques (data mining; visualization, interaction, etc.) Case studies User study (questionnaire-based; task-based) Expert Interview