This document provides an overview of a post-academic course on Big Data taught by Joris Klerkx. It discusses the Augment group's mission to augment human intellect through tools and technologies. Their research focuses on capturing physiological and behavioral signals through sensors to create meaningful feedback loops. Application domains discussed include technology-enhanced learning, media consumption, science, and health. Guidelines for visualizing big data emphasize using interactive visual encodings to promote recognition over recall by humans. Interactivity, overview first approaches, and checking data quality are advised.

1. Post-academic course Big Data
Post-academic course Big Data
Joris Klerkx
Research Manager, PhD.
joris.klerkx@cs.kuleuven.be
Visualisatie
Big Data
IVPV - Instituut voor PermanenteVorming
28-05-2015
1

2. Augment group - HCI research lab
Dept. Computerwetenschappen
KU Leuven
https://augmenthuman.wordpress.com
2

3. Erik Duval
11/9/1965 – 12/3/2016
3

4. Our mission
“To augment the human intellect” (Engelbart, 1962)
4
By ‘augmen+ng human intellect’ we mean increasing the capability of a man
to approach a complex problem situa+on, to gain comprehension to suit his
particular needs, and to derive solu+ons to problems.

5. Design, build and evaluate relevant tools and
technologies that help users to become beCer in
their daily life & work (Duval, 2015)
Our mission
5

6. What are relevant user
actions?
How can we capture signals?
How can we store them?
How can we create a
meaningful feedback loop?
Our Research
Physiological, behavioural signals
Sensors, (self-)trackers
Information visualization
Scalable infrastructure
6

7. Application Domains
Technology-Enhanced Learning
Media
Consumption
Science 2.0
(e)Health
7

8. Slides will be posted to Slideshare & Zephyr
8

9. http://www.hearts.com/ecolife/cut-paper-consumption-protect-forests/
9

10. Big Data
10

11. Big data
11

12. Big data
insights
12

13. Better Human
Understanding
13

14. A mental model represents what a person
thinks is true… but isn’t necessarily true
14

15. UNDERSTANDING OF THEIR MENTAL MODELS
15

16. Wouter Walgrave - http://www.slideshare.net/wouterwalgraeve/mental-models-as-information-radiators
16

17. 17

18. 18

19. ?
19

20. "The idea that business is strictly a numbers affair has always struck me as preposterous.
For one thing, I’ve never been particularly good at numbers, but I think I’ve done a
reasonable job with feelings. And I’m convinced that it is feelings — and feelings alone —
that account for the success of the Virgin brand in all of its myriad forms.” -- Richard
Branson
20

21. Gut feeling
21

22. What your gut
feeling says
What the
facts say
22

23. What your gut
feeling says
What the
facts say
Confirmation bias
Undervalued Overvalued Foolish
23

24. Big data
insights
data-driven insights
24

25. 25

26. Big data
insights
data-driven insights
Meaningful
26

27. Defining visualization
27

28. Definition
28
Information Visualization is the use
of interactive visual representations
to amplify cognition [Card. et. al]

29. algorithm
<>
human
29

30. Information Visualisation is the use
of interactive visual representations
to amplify cognition [Card. et. al]
Definition
30

31. http://www.demorgen.be/dm/nl/5403/Internet/article/detail/1890428/2014/05/18/Twitteractiviteit-verraadt-je-politieke-profiel.dhtml31

32. Facilitate human interaction
for exploration with and
understanding of big data
32

33. Data visualization
Slide source: John Stasko
Scientific
visualization
Information
visualization
33

34. Scientific visualisation
Specifically concerned with data that has a well-defined representation in 2D or 3D space (e.g., from
simulation mesh or scanner).
Slide source: Robert Putman 34

35. InformationVisualisation
Concerned with data that does not have a well-defined
representation in 2D or 3D space (i.e.,“abstract data”)
35

36. Dispersion (Backstrom & Kleinberg)
36

37. The role of visualisation
37

38. Big data
insights
data-driven insights
Meaningful
38

39. By Longlivetheux - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=3770524739

40. https://medium.com/@angelamorelli/3-powerful-lessons-i-have-learnt-as-an-information-designer-cb028940254#.mkgb0h2cc
40

41. The Role of visualisation
Brehmer, M.; Munzner, T., "A Multi-Level Typology of Abstract Visualization Tasks," Visualization
and Computer Graphics, IEEE Transactions on , vol.19, no.12, pp.2376,2385, Dec. 201341

42. Explore
Data insights: a visualization (Gregor Aisch)
42

43. http://www.visual-analytics.eu/faq
Also: Visual Analytics
43

44. Visualizing Big Data
44

45. Multiple data sources with varied data types
“Diverse” data
I talk geoJSON
i talk custom
xml
i talk apache
logs
45

46. millions of records
“Tall” data
46

47. http://dataclysm.org
Example: 51 million ratings
47

48. Example: 51 million ratings
48

49. http://dataclysm.org
Example: 51 million ratings
49

50. http://dataclysm.org
Example: 51 million ratings
50

51. http://dataclysm.org
51

52. Cluttered displays
Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)52

53. Cluttered displays
Binned density scatterplot
Hexagonal instead of rectangular
Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)53

54. Multi-variate data with 100s to 1000s of variables
“Wide” data
54

55. http://www.perceptualedge.com/blog/?p=2046
In this day of so-called Big Data,
organizations are scrambling to
implement new software and
hardware to increase the amount of
data that they collect and store.
In so doing they are unwittingly
making it harder to find the needles of
useful information in the rapidly
growing mounds of hay.
If you don’t know how to
differentiate signals from noise,
adding more noise only makes
matters worse.
55

56. Avoid the All-You-Can-Eat buffet! (Ben Fry)56

57. Visualizations might help reveal multidimensional patterns
Use the power of the machine to find a proxy in the data that
predicts the selected variables
Depending on their specific questions, domain experts might
select a subset of variables they are interested in
57

58. Example: 4 million messages/day on OKCupid
http://dataclysm.org
58

59. Each dot at 90% transparency
http://dataclysm.org
59

60. http://dataclysm.org
60

61. http://dataclysm.org
61

62. http://dataclysm.org
62

63. Multiple views on the data allow exploration of patterns
63

64. The strength of visualization
64

65. Anscombe`s quartet
http://en.wikipedia.org/wiki/Anscombe's_quartet
Enables discovery of visual patterns in data sets
Graphics reveal data (Tufte, 2001)
65

66. World Population Growth
A tremendous change occurred with the industrial revolution: whereas it had taken all of human history until
around 1800 for world population to reach one billion, the second billion was achieved in only 130 years
(1930), the third billion in less than 30 years (1959), the fourth billion in 15 years (1974), and the fifth billion in
only 13 years (1987). During the 20th century alone, the population in the world has grown from 1.65
billion to 6 billion.
Seeing is understanding
66

67. Facilitates understanding
http://www.bbc.co.uk/news/world-15391515
67

68. Facilitates human interaction for exploration and understanding
http://www.bbc.co.uk/news/world-15391515
68

69. http://www.informationisbeautiful.net/visualizations/how-many-gigatons-of-co2/
Tells stories
69

70. T. Nagel, M. Maitan, E. Duval,A.Vande Moere, J. Klerkx, K. Kloeckl, and C. Ratti.Touching transport - a case study on visualizing metropolitan public
transit on interactive tabletops. In AVI2014: 12th ACM International Working Conference on AdvancedVisual Interfaces, pages 281–288, 2014.
http://www.youtube.com/watch?v=wQpTM7ASc-w
Facilitates human interaction for exploration and understanding
70

71. Will there be enough food?
http://www.footprintnetwork.org/en/index.php/gfn/page/earth_overshoot_day/
Communicates insights easily
71Triggers Impact

72. http://terror.periscopic.com
Shows patterns & triggers questions
72

73. Interactivity allows comparison
73

74. http://blog.stephenwolfram.com/2012/03/the-personal-analytics-of-my-life/
Shows trends & anomalies in the data, therefore triggers questions
74

75. Helps to find stories, see trends
BelgiumBrazil
USA
India
75

76. Sentiment analysis in enterprise social network (slack)
Shows patterns
76

77. http://deredactie.be/cm/vrtnieuws/grafiek/interactief/1.224856177

78. Reader Client
Tracking Service
WebSockets
Database
engagement data mouse data
10.065 sessies werden getracked
9674 sessies werden gebruikt
in de analyse
391 sessies werden verwijderd
uit analyse (noise)
78

79. Visualizing Reader Activity
Elk vierkant is een ‘slide’
Elke rij stelt een
navigatie-patroon voor
doorheen de slides
Kolom 1 toont absoluut
aantal lezers
Kolom 2 toont het
percentage lezers
79

80. 262 readers (2.7%) gaan volledig door alle slides, waarna
ze snel teruggaan naar de eerste slide om die nog even
te bekijken.
Lezerstijd per slide
Lezers spenderen +/- 75 seconden (avg) op de eerste slide
om te bestuderen welke informatie voorhanden is.
80
Shows patterns

81. Sentiment analysis in enterprise social network (slack)
Triggers questions & creates awareness
Disclaimer: Should we trust NLP-algorithms?
81

82. Empowers users to make informed decisions
Positive Badges
Negative Badges
82

83. Show errors in the data
http://woutervds.github.io/InfoVisPostgraduwhat/83

84. Show errors in the data
84

85.
Khaled Bachour, Frederic Kaplan, Pierre Dillenbourg, "An Interactive Table for Supporting Participation Balance in Face-to-Face
Collaborative Learning," IEEE Transactions on Learning Technologies, vol. 3, no. 3, pp. 203-213, July-September, 2010
Creates awareness
85

86. http://infosthetics.com/
http://visualizing.org
http://www.visualcomplexity.com/vc/
http://visual.ly/
http://flowingdata.com
http://www.infovis-wiki.net
86

88. Visualizing (big) data
Guidelines & Facts
88

89. How many circles?
89

90. Humans have advanced perceptual abilities
Our brains makes us extremely good at recognizing visual patterns
90

91. 91
Humans have little short term memory
Our brain remembers relatively little of what we perceive.

92. Most of us can only hold three to seven chunks of data at the same time.
Humans have little short term memory
92

93. Recognition
Identify previously learned information
93

94. Humans have advanced perceptual abilities
Humans have little short term memory
Our brains makes us extremely good at recognizing visual patterns
Our brains remember relatively little of what we perceive
Externalize data by using interactive, visual encodings
Promote recognition rather than recall
94

95. https://www.youtube.com/watch?v=og7bzN0DhpI (9:51 - 11:22 )95

96. 96

97. “The centrality of human
activity in the process is key”
97

98. Explore
Data insights: a visualization (Gregor Aisch)
98

99. “It’s not a magical algorithm
that finds the insight for you”
“You have to look at the overview,
you have to decide what you zoom
in to, what you filter out. And then
you click to get the details”
Ben Shneiderman, 201199

100. http://www.bbc.com/future/bespoke/20140724-flight-risk/
Overview first, zoom & filter, details-on-demand
100

101. Overview first, zoom & filter, details-on-demand
http://www.student.kuleuven.be/~r0580868/
101

102. https://postgraduwhatblog.wordpress.com/2016/02/13/infovis-van-de-week-1-wouter/
Overview first, zoom & filter, details-on-demand
102

103. Visual Information Seeking Mantra
103

104. Real data is ugly and needs to be cleaned
http://hcil2.cs.umd.edu/trs/2011-34/2011-34.pdf
http://www.netmagazine.com/features/seven-dirty-secrets-data-visualisation
https://code.google.com/p/google-refine/
http://vis.stanford.edu/wrangler/Pre-process your data
104

105. http://nieuws.vtm.be/verkiezingen/gemeente?province=P1&city=G73
Always check & pre-process your data
105
Verkiezingen
14/10/12

106. Forget about 3D graphs (on a 2D screen..)
Occlusion
Complex to interact with
Doesn’t add anything to the data
106

107. Source: Stephen Few
What if we need to add a 3rd variable?
107

108. Use small coordinated graphs to add variables
108
Forget about 3D graphs
Source: Stephen Few

109. Which student has more blogposts?
• Size & angle are difficult to compare
• Without labels & legends, impossible to show exact quantitative
differences
• Limited Short term (visual) memory
109

110. Source: Stephen Few
Save the pies for dessert (S. Few)
Try using either of the pies to put the slices in order by size
110

111. deredactie.be
demorgen.be
vtm.be
Verkiezingen
14/10/12
111

112. Obviously there are exceptions to the rule
112
http://themetapicture.com/the-sunny-side-of-the-pyramid/

113. 0"
5"
10"
15"
20"
25"
30"
blogposts" tweets" comments"on"
blogs"
reports"
submi6ed"
Student'1'
Student"1"
0" 5" 10" 15" 20" 25" 30"
blogposts"
comments"on"blogs"
tweets"
reports"submi6ed"
Student'1'
Student"1"
Use Common Sense
0"
5"
10"
15"
20"
25"
30"
blogposts" comments"on"
blogs"
tweets" reports"
submi6ed"
Student'1'
Student"1"
113

114. 0" 10" 20" 30" 40" 50" 60"
Student"1"
Student"2"
Student"3"
Student"4"
blogposts"
tweets"
comments"on"blogs"
reports"submi:ed"
0%# 20%# 40%# 60%# 80%# 100%#
Student#1#
Student#2#
Student#3#
Student#4#
blogposts#
tweets#
comments#on#blogs#
reports#submi;ed#
Use Common Sense
What are you comparing?
What story do you get from it?
114

115. Which graph makes it easier to focus on the pattern of change
through time, instead of the individual values?
Choose graph that answers your questions about your data
115Source: Stephen Few

116. vtm.be
deredactie.be
nieuwsblad.be
Verkiezingen
14/10/12
Communicate the correct story
116

117. Don’t use visualisations to mislead
117

118. Don’t use visualisations to mislead
118

119. Source: Stephen Few
119

120. Source: Stephen Few 120

121. 121

122. http://fellinlovewithdata.com/research/deceptive-visualizations
122

123. http://fellinlovewithdata.com/research/deceptive-visualizations
123

124. How much better are the drinking water conditions in Willowtown as
compared to Silvatown?
124
http://fellinlovewithdata.com/research/deceptive-visualizations

125. Storytelling with visualisation
125

126. Visualization tasks
Brehmer, M.; Munzner, T., "A Multi-Level Typology of Abstract Visualization Tasks," Visualization
and Computer Graphics, IEEE Transactions on , vol.19, no.12, pp.2376,2385, Dec. 2013126

127. http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html
127

128. Human Perception
128
Our brains makes us extremely good at recognizing visual patterns

129. Source: Katrien Verbert 129

130. Source: Katrien Verbert 130

131. A limited set of visual properties that are detected
- very rapidly (< 200 to 250 ms),
- accurately,
- with little effort,
- before focused attention
by the low-lever visual system on them.
Healey, C., & Enns, J. (2012). ADenEon and Visual Memory in VisualizaEon and Computer Graphics. IEEE Transac+ons on Visualiza+on
and Computer Graphics , 18 (7), 1170-1188.
Pre-attentive characteristics
Note that eye movements take at least 200 ms to initiate.
131

132. Pre-attentive characteristics
Find the red dot
<> Hue
Find the dot
<> shape
Find the red dot
conjunction
not pre-attentive
http://www.csc.ncsu.edu/faculty/healey/PP/
helps to spot differences in multi-element display
132

133. Pre-attentive characteristics
Line orientation Length, width Closure Size
Curvature Density, contrast Intersection 3D depth
Not all of them allow showing exact quantitative differences
Helps to spot differences in multi-element display
133
http://www.csc.ncsu.edu/faculty/healey/PP/

134. http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
http://artspilesenglish.blogspot.be/2011/11/gestalt-theory-exercise-for-3rdlevel.html
134
Gestalt Laws (“Pattern” laws)
Basic rules or design principles that describe perceptual phenomena.
Explain the way users or humans see patterns in visualisations.

135. Figure & Ground
135

136. 136
Closure

137. Smallness
137Source: Katrien Verbert

138. Common Fate
Objects with a common movement, that move in the same
direction, at the same pace, at the same time are organised as a
group (Ehrenstein, 2004).
138

139. Law of Isomorphism
Is similarity that can be behavioural or perceptual, and can
be a response based on the viewers previous experiences
(Luchins & Luchins, 1999; Chang, 2002).This law is the basis
for symbolism (Schamber, 1986).
139

140. London Tube Map
Which Gestalt laws do you see?
140

141. Visualization design process
141

142. B. McDonnel and N. Elmqvist. Towards utilizing gpus in information visualization:A model and implementation of
image-space operations.Visualization and Computer Graphics, IEEE Transactions on, 15(6):1105–1112, 2009.
http://www.infovis-wiki.net/index.php/Visualization_Pipeline
142

143. 143

144. Data
- structure
time, hierarchy, network, 1D, 2D, nD, …
- questions
where, when, how often, …
- audience
domain & visualisation expertise, …
144

145. S. Stevens. On the theory of scales of measurement. Science, 103(2684), 1946.
Structure
Time? hierarchical? 1D? 2D? nD? network? …
145

146. Questions (to get things going)
What is the average amount of students that bought the course book ?
What? When? How much? How often?
When did students start looking at the course material?
How much hours did Peter work on this assignment?
(Why did Peter have to redo his assignment?)
How often did Peter retake the course before he passed?
(why?)
146

147. 147
Visual mapping
Encode data characteristics into visual form
Each mark (point, line, area,…) represents a data element
Think about relationships between elements (position)
“Simplicity is the ultimate sophistication.”
Leonardo daVinci

148. Size
http://www.informationisbeautiful.net/2009/visualising-the-guardian-datablog/
148

149. X 4
How much bigger is the lower bar?
Slide adapted from Michael Porath & Katrien Verbert
Length
149

150. X 5
How much bigger is the right circle?
Slide adapted from Michael Porath & Katrien Verbert
Area
150

151. X 9
How much bigger is the right circle?
151

152. Apparent magnitude curves
http://makingmaps.net/2007/08/28/perceptual-scaling-of-map-symbols
Slide adapted from Michael Porath
152

153. Which one looks more accurate?
Slide adapted from Michael Porath
153
Compensating magnitude to match perception

154. Color
Color Principles - Hue, Saturation, andValue
https://www.youtube.com/watch?v=l8_fZPHasdo154
Use maximum +/- 5 colors (for categories,.. ) (short term memory)
http://en.wikipedia.org/wiki/HSL_and_HSV

155. • hue: categorical
• saturation: ordinal and quantitative
• luminance/brightness:
ordinal and quantitative
How to choose colors
source from: Katrien Verbert 155

156. http://colorbrewer2.org
156

157. 157

158. https://eagereyes.org/basics/rainbow-color-map
158

159. http://gizmodo.com/why-a-white-cup-makes-your-coffee-taste-more-intense-1663691154
intensity, sweetness, aroma, bitterness, and quality
159
How to choose colors

160. Position
160

161. Position & color
http://time.com/12933/what-you-think-you-know-about-the-web-is-wrong/
161

162. J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141, 1986.
162

163. 163
J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141,
1986.

164. 164

165. Offer precise controls for sharing on the Internet...
Users should navigate through 50 settings with more than 170 options
Example
Facebook privacy statement
Questions?
How did its complexity change over time?
How does its length compare to privacy statements
of other tools?
165

166. How did its complexity change over time?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html
166

167. How does its length compare to privacy statements
of other tools?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html
167

168. Example:
Encoding weather forecast on a smartphone
168

169. ?
Joris Klerkx
Research Manager, PhD.
joris.klerkx@cs.kuleuven.be
@jkofmsk
https://augmenthuman.wordpress.com
169
Always on-the-look for new opportunities…