visualisation - techniques, interaction dynamics, big data
TRANSCRIPT
Post-‐academic course Big Data
Post-‐academic course Big Data
Joris KlerkxResearch Expert, [email protected]@jkofmsk
Erik [email protected]@erikduval
Visualisatie - deel 2Big Data - module 3IVPV - Instituut voor Permanente Vorming28-05-2015
To research, design, create and evaluate useful tools that augment the human intellect
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 (Douglas Engelbart, 1962).
Augment group - HCI research lab Dept. ComputerwetenschappenKU Leuvenhttps://augmenthuman.wordpress.com
Music
Technology Enhanced Learning
e-health
Research 2.0
HealthMedia
(Consumption)
Technology Enhanced Learning
Science 2.0
https://augmenthuman.wordpress.com/
http://eng.kuleuven.be/datavislab/
Recap
Humans have advanced perceptual abilities
Humans have little short term memory
Externalize data by using interactive, visual encodings
Our brains makes us extremely good at recognizing visual patterns
Our brains remember relatively little of what we perceive
Before visualisation…
Data
- structuretime, hierarchy, network, 1D, 2D, nD, …
- questions where, when, how often, …
- audience domain & visualisation expertise, …
Audience vs purpose
Explore Explain
Lay person
Domain experts
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 da Vinci
J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141, 1986.
Many ways to visualize simple dataset
Some ways are more effective than others, depending on your question at hand
http://gravyanecdote.com/visual-analytics/which-chart-should-you-use-to-show-this-data/
Information Visualisation is the use of interactive visual representations to amplify cognition [Card. et. al]
Definition
Information visualization significantly improves insight generations and user productivity
Accelerates time to insight
Today
• Tour through the visualisation zoo
• Interactive Dynamics
• Is there too much data to visualize?
• Tools?
A tour through the visualization zoo
Heer, J., Bostock, M., & Ogievetsjy, V. (2010, May). A Tour through the VisualizaMon Zoo -‐ A survey of powerful visualisaMon techniques, from the obvious to the obscure. ACM Graphics , 8 (5), hTps://queue.acm.org/detail.cfm?id=1805128
Time-series dataSets of values changing over time
Relative changes in time-series data
An index chart is an interactive line chart that shows percentage changes for a collection of time-series data based on a selected index point.
http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/ti
me/
inde
x-ch
art.h
tml
Aggregated time-series data
A stream graph visually summates time-series values
http
://hc
i.sta
nfor
d.ed
u/jh
eer/fi
les/
zoo/
ex/ti
me/
stac
k.ht
ml
Time-series data
Time-series data
Small multiples: showing each series in its own chart.
http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/ti
me/
mul
tiple
s.ht
ml
https://augmenthuman.wordpress.com/portfolio/visualizing-gaming-trends-on-steam/
http://bl.ocks.org/oyyd/859fafc8122977a3afd6
Day-Hours Heatmap
http://www.trulia.com/vis/tru247/
The horizon graph is a technique for increasing the data density of a time-series view while preserving resolution.
Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations Jeffrey Heer, Nicholas Kong, Maneesh Agrawala ACM Human Factors in Computing Systems (CHI), 2009. pp. 1303 - 1312. Best Paper Award PDF (442K)
http://homes.cs.washington.edu/~jheer//files/zoo/ex/time/horizon.html
http://www.perceptualedge.com/blog/?p=390
A year’s worth of stock prices for 50 separate stocks
http://neuralengr.com/asifr/journals/
Publication counts over time
Statistical DistributionsReveal how a set of numbers is distributed and thus help an analyst better understand the statistical properties of the data
Histograms shows the prevalence of values grouped into bins
Histogram sliders
A stem-and-leaf plot bins numbers according to the first significant digit, and then stacks the values within each bin by the second significant digit.
http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/s
tats
/ste
m-a
nd-le
af.h
tml
http://thesocietypages.org/graphicsociology/2012/11/18/stem-and-leaf-diagrams/
Box-and-whisker plot, which can convey statistical features such as the mean, median, quartile boundaries, or extreme outliers.
http://admin-apps.webofknowledge.com/JCR/help/h_boxplot.html
Statistical distribution of data
The Q-Q plot compares two probability distributions by graphing their quantiles against each other.
http://hci.stanford.edu/jheer/files/zoo/ex/stats/qqplot.html
Representing relationships/correlations among multiple variables.
A scatter plot matrix (SPLOM) uses small multiples of scatter plots showing a set of pairwise relations among variables
http://homes.cs.washington.edu/~jheer//files/zoo/ex/stats/splom.html
graphing every pair of variables in two dimensions
http://homes.cs.washington.edu/~jheer//files/zoo/ex/stats/parallel.html
MapsMostly based upon a cartographic projection: a mathematical function that maps the three-dimensional geometry of the Earth to a two-dimensional image
Other maps knowingly distort or abstract geographic features to tell a richer story or highlight specific data.
http://geoawesomeness.com/topics/web-maps/http://unfoldingmaps.orghttp://ffffound.com/home/tillnm/found/
• Google Maps - Well rounded, established mapping solution, especially for non-developers to get a basic map on the web, along with all the powers that Google is (in)famous for.
• OpenLayers - For situations when other mapping frameworks can’t solve your spatial analysis problems.
• Leaflet - Currently, easily the best mapping framework for general mapping purposes, especially if you don’t need the additional services that MapBox or CartoDB provide.
• MapBox - Fast growing and market changing mapping solution for when you want more control over map styling or have a need for services that others are not providing, such as detailed satellite images, geocoding or directions.
• Unfolding - to create interactive maps and geovisualizations in Processing and Javahttp://www.toptal.com/web/the-roadmap-to-roadmaps-a-survey-of-the-best-online-mapping-tools
Typical Mapping Tools
http
://en
.wik
iped
ia.o
rg/w
iki/J
ohn_
Snow
_(ph
ysic
ian)
#/m
edia
/File
:Sno
w-c
hole
ra-m
ap-1
.jpg
John Snow, Cholera, 1854
A flow map depicts the movement of a quantity in space and (implicitly) in time.
Charles Joseph Minard, 1781-1870 Napoleon’s Russian Campaign
https://lirias.kuleuven.be/bitstream/123456789/461689/1/paper234_camera-ready.pdf
http://homes.cs.washington.edu/~jheer//files/zoo/ex/maps/choropleth.html
Choropleth Maps use color encoding to show aggregated data by geographical areas
Choropleth maps using 2 categories
http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
Choropleth maps using 5 categories
http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
Choropleth maps using 9 categories
http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
Choropleth maps using different ranging methods
Equal Range
http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
User Defined Range
http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
Quantile Range
http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
http://www.nieuwsblad.be/cnt/dmf20150603_01712465
Graduated Symbol Maps places symbols/glyphs over an underlying map
http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/m
aps/
sym
bol.h
tml
Graduated Symbol Maps places symbols over an underlying map
A cartogram distorts the shape of geographic regions so that the area directly encodes a data variable
http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/m
aps/
carto
gram
.htm
l
http://www.viewsoftheworld.net/?p=2541
Another cartogram?
There is no perfect map
How could you actually compare sizes of different continents and countries?
https://www.youtube.com/watch?v=KUF_Ckv8HbE
HierachiesMost data can be organised into natural hierarchies
Special visualization techniques exist to leverage hierarchical structure, allowing rapid multiscale inferences: micro-observations of individual elements and macro-observations of large groups
A node-link diagram with Reingold-Tilford algorithmht
tp://
hci.s
tanf
ord.
edu/
jhee
r/file
s/zo
o/ex
/hie
rarc
hies
/tree
.htm
l
The dendrogram (or cluster) algorithm places leaf nodes of the tree at the same level
Polar coordinates instead of cartesian coordinates
http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/h
iera
rchi
es/c
lust
er-ra
dial
.htm
l
Indented tree layout
http://homes.cs.washington.edu/~jheer//files/zoo/ex/hierarchies/indent.html
The adjacency diagram is a space-filling variant of the node-link diagram; rather than drawing a link between parent and child in the hierarchy, nodes are drawn as solid areas (either arcs or bars), and their placement relative to adjacent nodes reveals their position in the hierarchy
http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/h
iera
rchi
es/ic
icle
.htm
l
The sunburst layout, shown in figure 4E, is equivalent to the icicle layout, but in polar coordinates. ht
tp://
hom
es.c
s.w
ashi
ngto
n.ed
u/~j
heer
//file
s/zo
o/ex
/hie
rarc
hies
/sun
burs
t.htm
l
Polar Area Diagram
Enclosure diagrams use containment rather than adjacency to represent the hierarchy
Squarified Treemaps - space filling http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/h
iera
rchi
es/tr
eem
ap.h
tml
Circle-packing layoutNon - space filling
http
://hc
i.sta
nfor
d.ed
u/jh
eer/fi
les/
zoo/
ex/h
iera
rchi
es/p
ack.
htm
l
NetworksNon-hierarchical relationships between data points
https://queue.acm.org/detail.cfm?id=1805128
A force-directed layout where nodes are charged particles that repel each other, and links are dampened springs that pull related nodes together.
http
://hc
i.sta
nfor
d.ed
u/jh
eer/fi
les/
zoo/
ex/n
etw
orks
/forc
e.ht
ml
http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/n
etw
orks
/arc
.htm
lAn arc diagram uses a one-dimensional layout of nodes, with circular arcs to represent links.
http://www.youtube.com/watch?v=wQpTM7ASc-w
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 Advanced Visual Interfaces, pages 281–288, 2014.
http
://ho
mes
.cs.
was
hing
ton.
edu/
~jhe
er//fi
les/
zoo/
ex/n
etw
orks
/mat
rix.h
tml
Adjacency matrix: each value in row i and column j in the matrix corresponds to the link from node i to node j.
Chord diagrams show directed relationships among a group of entities. Relationship can be quantitative or binary
http://bl.ocks.org/mbostock/4062006 Ye L, Amberg J, Chapman D et al. 2013 Fish gut microbiota analysis differentiates physiology and behavior of invasive Asian carp and indigenous American fish The ISME journal
A Circo
http://circos.ca/guide/tables/img/guide-table-large.png
Choices of representation (e.g., matrix- diagram) and interactive parameterization (e.g., default sort order) can be critical to unearthing data quality issues that can otherwise undermine accurate analysis.
Facebook friends
Hee
r & K
ande
l, 20
12. I
nter
activ
e an
alys
is o
f big
dat
a. M
anag
es X
RDS:
Cro
ssro
ads,
The
AC
M
Mag
azin
e fo
r Stu
dent
s - B
ig D
ata,
19
(1),
50-5
4. h
ttp://
dl.a
cm.o
rg/c
itatio
n.cf
m?
id=2
3310
42.2
3310
58
Facebook friends
Hee
r & K
ande
l, 20
12. I
nter
activ
e an
alys
is o
f big
dat
a. M
anag
es X
RDS:
Cro
ssro
ads,
The
AC
M
Mag
azin
e fo
r Stu
dent
s - B
ig D
ata,
19
(1),
50-5
4. h
ttp://
dl.a
cm.o
rg/c
itatio
n.cf
m?
id=2
3310
42.2
3310
58
Facebook friends
Hee
r & K
ande
l, 20
12. I
nter
activ
e an
alys
is o
f big
dat
a. M
anag
es X
RDS:
Cro
ssro
ads,
The
AC
M
Mag
azin
e fo
r Stu
dent
s - B
ig D
ata,
19
(1),
50-5
4. h
ttp://
dl.a
cm.o
rg/c
itatio
n.cf
m?
id=2
3310
42.2
3310
58
Many(!!) more techniques are out there
e.g. check the visual index at https://github.com/mbostock/d3/wiki/Gallery
http://www.visual-literacy.org/periodic_table/periodic_table.html
https://eagereyes.org/blog/2009/
visualization-is-not-periodic-html
When“static”visualizations are not enough
Too many datapoints or variables
True understanding and insight generation require interaction
Data is exploratory in nature
Interaction
Should contribute to successful analytic dialogues!
Support exploration at the rate of human thought!
Interactive Dynamics enables analysts to explore large data sets (Task types)
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
• Data & View Specification controls • View Manipulation • Process & Provenance
Data & View Specification
Custom programming of a specific visualization component
Specific tools that use a chart typology (eg excel)
Data-flow graphs deconstruct the visualization process into fine-grained set of operators (data import, transformation, layout, coloring, etc
Visualization construction using formal grammars (eg ggplot2, R, Prototvis, etc)
Data & View Specification controls serve to visually & interactively encode data
Custom programming “hacking”
Processing demo
google spreadsheets
Tools that use a chart typology (eg excel)
Excel for mac 2015
Tools that use a chart typology (eg excel)
http://www-969.ibm.com/software/analytics/manyeyes/
Cfr. http://gravyanecdote.com/visual-analytics/which-chart-should-you-use-to-show-this-data/
Data-flow graphs deconstruct the visualization process into fine-grained set of operators (data import, transformation, layout, coloring, etc
http://datacollider.io/
Let me know how this works for you ;)
View Manipulation
• Categorical/ordinal data
• radio buttons, checkboxes, scrollable lists, hierachies, search boxes (with autocomplete)
• Ordinal, quantitative, and temporal data
• a standard slider (for a single threshold value) or a range slider (for specifying multiple endpoints).
Filtering allows rapid and reversible exploration of data subsets
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
http
://do
ug.c
s.ku
leuv
en.b
e/w
ittge
nste
in2/
http://localhost:63342/DataAnalysis/report/dre-userpaths_filters.html
http://www.trulia.com/vis/tru247/
Query controls can be further augmented with visualizations of their own
Query controls can be further augmented with visualizations of their own
Sorting enables popping up of trends, clusters,…
• Choices in a toolbar
• Clicks on the header in a table
• Can be complicated in the case of multiple view displays
http://terror.periscopic.com
https://augmenthuman.wordpress.com/portfolio/emotions-in-the-classroom/
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Select items to hightlight, filter or manipulate them
• Mouse clicks, free-form lassos, area cursors (‘brushes’), mouse hovering, etc • depends on the device
• Various expressive power • selections of a collection of items • selections as queries over the data (eg drawing
rectangle -> range query)
https://perswww.kuleuven.be/~u0074988/mapc/
Selection queries
https://augmenthuman.wordpress.com/portfolio/mapc_interactive_dashboard/
Select by slope and tolerance
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Mapping mouse gestures to query patterns
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Navigate to examine high-mede patterns & low-level detail
• Overview first, zoom & filter, then details-on-demand • Start with what you know, then grow
• Search, show context, expand on demand. • Focus + Context • Semantic Zooming • Magical lenses
$
are just by visually looking for the largest number of con-nected nodes. These larger clusters can be a first indicationof where high profile authors are located. However, in thisstate, neither the names of the authors nor the titles of thepapers are visible yet.
When the user wants to look into more details, he can zoomin to a specific part of the publication space. This is whatFigure 3 depicts. The author names become clearly visible,so that the user can identify a particular author. The usercan also click on paper nodes to get more information on thepaper. To make it easier to identify which authors are moreprolific in the field, the node size of the author is directlyproportional to his number of publications. In Figure 3, forexample, author Martin Wolpers has the largest number ofpublications and is a good candidate to use as a landmarkin the exploration process.
4. EVALUATIONIn this section, we describe how we have evaluated our firstiteration. Subsections 4.1 and 4.2 elaborate on the setupof the evaluation. Subsection 4.3 discusses the results ofthe evaluation and finally, in subsection 4.4, we draw ourconclusions from this evaluation.
4.1 DescriptionTo evaluate the application, we deployed our tabletop in themain hall of the ECTEL 2010 conference [42]. This roomwas the main location for co�ee breaks and figure 4 illus-trates the tabletop setup.The evaluation was conceived as a formative evaluation, inorder to gather feedback on the design and implementationof the application from real users in a real life scenario. Wefollowed the think aloud method, where the participantsverbally describe their thoughts during the evaluation. Inthis way, the participants reveal their view on the systemand possibly their misconceptions [28]. It started o� withgeneral questions (age, gender, profession, vision and leftor right handed) about the participants together with theirbackgrounds. The participants were introduced to the ap-plication by asking them if they could explain what theysaw. We also asked them one basic content-related ques-tion to get them started: “Find author x and find out howmany papers he wrote in ECTEL 2007”. When needed, theparticipants were given extra explanation about the appli-cation. After this, the evaluation continued with tasks theyhad to perform. For each task, we noted whether the tasksucceeded, how fluently the task was performed and whetherthe participant needed help or not. Finally, the participantswere asked for some general feedback and they filled in asmall questionnaire about usefulness and ease of use. Eachevaluation took between 20 and 30 minutes.
4.2 ParticipantsThere was a total of 11 participants, aged between 27 and 60.All participants were researchers, right handed and all butone had corrected vision. Only 3 of the participants con-sidered that they had a bit of experience with multitouchinteraction, the other 8 said they had a lot of experience.Regarding experience towards tabletops or multitouch wallshowever, only one person described himself as experienced.To find out how experienced the participants were in the
Figure 4: Setting of the evaluation.
Figure 5: An overview of the number of papers theparticipants have written
research area, they were asked about their years of experi-ence in the Technology Enhanced Learning (TEL) researcharea, the number of papers published and how many of thempublished in TEL. Half of the participants claimed to haveup to 3 years of experience and the other half claimed tohave many years of experience. On average, the partici-pants have published around 32 papers, from which 16 inthe TEL area. Three participants have published more than60 papers, from which 20 or more in the TEL area. Fig-ure 5 shows in detail the number of published papers perparticipant.
4.3 ResultsIn this section, we describe the results of the evaluation.These results are grouped in three parts. First, we reporton the tasks the participants had to perform, second, wesummarize the most important feedback, and third, we takea look at the results from the questionnaire.
4.3.1 Tasks
B. Vandeputte, E. Duval, and J. Klerkx. Interactive sensemaking in authorship networks. Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces, ITS11, pp. 246–247, 2011.
Overview first, zoom and filter, details on demand
B. Vandeputte, E. Duval, and J. Klerkx. Applying design principles in authorship networks-a case study. In CHI EA’12: Proceedings of the 2012 ACM annual conference extended abstracts on Human Factors in Computing Systems, pages 741–744, 2012. (https://www.youtube.com/watch?v=R5CeTEejdBA)
Start with what you know, then grow Search, show context, expand on demand
http://sanfrancisco.crimespotting.org
Overview first, zoom and filter, details on demand
Viewport on the data: zooming & panning controls
Focus+context
Large hierarchies
https://philogb.github.io/jit/static/v20/Jit/Examples/Hypertree/example1.html
Limited screen space
Focus + Context Semantic Zooming
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Magical Lenses
C. Tominski, S. Gladisch, U. Kister, R. Dachselt, and H. Schumann. A Survey on Interactive Lenses in Visualization. EuroVis State-of-the-Art Reports, Swansea, UK, Eurographics Association, 2014.
Fisheye
C. Tominski, S. Gladisch, U. Kister, R. Dachselt, and H. Schumann. A Survey on Interactive Lenses in Visualization. EuroVis State-of-the-Art Reports, Swansea, UK, Eurographics Association, 2014.
Coordinate views for linked, multi-dimensional exploration
Enables seeing data from different perspectives
Multiple views can facilitate comparison
http://square.github.io/crossfilter/
https://perswww.kuleuven.be/~u0074988/mapc/https://augmenthuman.wordpress.com/portfolio/mapc_interactive_dashboard/
Small multiples
https://queue.acm.org/detail.cfm?id=2146416
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
https://augmenthuman.wordpress.com/portfolio/an-interactive-visualization-of-butterfly-observations/https://www.youtube.com/watch?v=NvqJi8NwLCI
Organize multiple windows & workspaces
• Tiled approaches (different widgets) allows to see all information and selectors at once, minimizing distracting scrolling or window operations, while enabling analysts to concentrate on extracting and reporting insights.
• Layout organization tools will become decisive factors in creating effective user experience
Orchestrate attention and mentally integrate patterns among viewsHeer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Orchestrating visualizations over various devices
In-class display
Process & Provenance
Record analysis histories for revisitation, review and sharing
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Annotate patterns to document findings
Record, organize, and communicate insights gained during visual exploration
Freeform graphical annotations without explicit tie to the underlying data
Data-aware annotationsHeer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Share views and annotations to enable collaboration
Real-world analysis is very much a social process that may involve multiple interpretations, discussion, and dissemination of results.
https://www.youtube.com/watch?v=g91_EBxVh3E
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
http://www-969.ibm.com/software/analytics/manyeyes/ https://www.tableau.com/products
Many other tools!
Sharing datasets!
Guide users through analysis tasks or stories
• Incorporate guided analytics to lead analysts through workflows for common tasks.
• Narrative visualization
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Artikel - tekst
Visualisatie
Narrative visualization
Interval 1 Interval 2 Interval 3 Interval 4
Interactive Dynamics: Summary
Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
Further notes
How much “big” data can we visualise?Petabytes? Exabytes? Yottabytes?!
Direct visualisation of big data ‘in the raw’ is probably not so effective
Is there too much data to visualize?
You only have so many pixels on a screenEach pixel, one data point
E.g Typical hdtv screen contains 1920 * 1080 = 2.073.600 pixels
prysm.com
https://vimeo.com/49679699
What is big data?
Multiple data sources with varied data types
“Diverse” data
I talk geoJSON
i talk custom xml
i talk apache logs
millions of records
“Tall” data
Cluttered displays
Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
Cluttered displaysBinned density scatterplot
Hexagonal instead of rectangular
Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
Perceptual scalability of a display should be limited by the chosen resolution of the data, not the numbers of records (Heer & Kandel, 2012)
Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
Multi-variate data with 100s to 1000s of variables
“Wide” data
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
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.
Monday, June 1st, 2015
When we rely on data for decision making, how do we tell what qualifies as a signal and what is merely noise? In and of itself, data is neither. It is merely a collection of facts. When a fact is true, useful, and deserves a response, only then is it a signal. When it isn’t, it’s noise. It’s that simple (Few, http://www.perceptualedge.com/blog/?p=2046, 2015)
Avoid the All-You-Can-Eat buffet! (Ben Fry)
Visual-information Seeking Mantra
Visual Analytics Mantra
Overview First, Zoom and Filter, Details-on-Demand
Analyze First, Show the Important, Zoom and Analyse Further, Details-on-Demand
Ben Shneiderman
Daniel Keim
Interactive analysis tools can help quell “big data” by augmenting our ability to manipulate and reason about it (Heer & Kandel, 2012)
Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
In the face of a data deluge, what remains relatively constant is our own cognitive ability to make sense of the data and reach reliable, informed decisions. Big data is of little help when decoupled from sound judgment.
J. Heer
Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
Large data is a wild beast and you’d better treat it with the right tools. Visualization is a great tool to convey what automatic data analysis algorithms discover. And often it is a very challenging task! What the algorithms spit is exciting new complex data that requires creativity and knowledge as well.
E. Bertini
http://fellinlovewithdata.com/guides/how-do-you-visualize-too-much-data
▪ Interactive visualization of a million items J.D. Fekete and C. Plaisant.
▪ Random Sampling as a Clutter Reduction Technique to Facilitate Interactive Visualisation of Large Datasets G. Ellis (part of it in collab. with yours truly).
▪ A Sampling Approach to Deal with Cluttered Information Visualizations E. Bertini (my phd thesis).
▪ TreeJuxtaposer: Scalable Tree Comparison using Focus+Context with Guaranteed Visibility T. Munzner, F. Guimbretiere, S. Tasiran, L. Zhang, and Y. Zhou.
▪ Beyond visual acuity: the perceptual scalability of information visualizations for large displays B. Yost, Y. Haciahmetoglu, and C. North.
▪ Extreme visualization: squeezing a billion records into a million pixels B. Shneiderman.
▪ Measuring Data Abstraction Quality in Multiresolution Visualization Q. Cui, M. O. Ward, E. A. Rundensteiner, and J. Yang.
• imMens: Real-time Visual Querying of Big Data Zhicheng Liu, Biye Jiang, Jeffrey Heer
Some papers about big data visualisation
http://fellinlovewithdata.com/guides/how-do-you-visualize-too-much-data
Tools for visualisation
http
://se
lect
ion.
data
visu
aliz
atio
n.ch
Don’t start from the tool
Start from your dataand the questions about your data
https://www.youtube.com/watch?v=vc1bq0qIKoA
The need for multidisciplinary teams
http://holisticsofa.com/
The Data Visualization Pyramid
POINTERS
• http://wearecolorblind.com/articles/quick-tips/
• http://infosthetics.com
• http://www.visualcomplexity.com/vc/
• http://bestario.org/research/remap
• ... (a lot more online! )
BOOKS• “Readings in Information Visualization: Using Vision to Think”,
Card, S et al.
• “Signals”, “Now i see”, “Show Me the Numbers”, Few, S.
• “Beautiful Evidence”, Tufte, E.
• “Information Visualization. Perception for design”, Ware, C.
• Beautiful Visualization: Looking at Data through the Eyes of Experts (Theory in Practice): Julie Steele, Noah Iliinsky
?Joris KlerkxResearch Expert, [email protected]@jkofmsk
https://augmenthuman.wordpress.com