visual moritz stefaner tools - well-formed...
TRANSCRIPT
Moritz Stefaner
Kognitionswissenschaftler
UI designer + Informationsvisualisierer
Info geek
Salon | Ludwig Boltzmann Institut Linz | 10.04.2008
VISUALTOOLSFOR THE
SOCIOSEMANTIC
WEB
THEMEN Visualisierung und Informationsaesthetik
Das Web und die Ordnung der Dinge
Thesis: „Visual tools for the socio–semantic web”
EU Projekt MACE
INFOGRAFIK
Daten Bild
Gestalter
CHARLES MINARD: NAPOLEON’S MARCH TO MOSCOW
INTERAKTIVE VISUALISIERUNG
DatenInformation
BildAnwendungErlebnisDing
User
Abbildungs-prozess
Designer
VISUALISIERUNG VON DATENSTRUKTUREN
the files and directories is used, where the levels in the hierarchy are shown by means
of indentation. The number of files and directories that can be shown simultaneously is
limited, which is no problem if one knows what to search for. However, if we want to
get an overview, or want to answer a more global question, such as: ”Why is my disk
full?”, scrolling, and opening and closing of subdirectories have to be used intensively.
During this process it is hard to form a mental image of the overall structure [3].
Many techniques have been proposed to visualize such structures more effectively.
An important category are node and link diagrams (fig. 1(a)). Elements are shown as
nodes, relations are shown as links from parent to child nodes. Sophisticated techniques
have been presented to improve the efficiency and aesthetic qualities of such diagrams,
both in 2D and in 3D [7, 5, 1, 2, 8]. Such diagrams are very effective for small trees, but
usually fall short when more than a couple of hundred elements have to be visualized si-
multaneously. The main reason for this limitation is simply that node and link diagrams
use the display space inefficiently: Most of the pixels are used as background. Treemaps
a16
e1 f2 g2 h4 i4
b3 c3 d10
j1 k1 l1 m1 n1 o1
(a) Tree diagram
e1
f2
c3
h4
j1 k1
l1 m1 n1 o1
(b) Treemap
Fig. 1. Tree diagram
[9, 6] were developed to remedy this problem. The full display space is used to visualize
the contents of the tree. Here we present an overview of the concept, an in depth treat-
ment is given in the original references. Figure 1(b) shows an example. Each node (as
shown in the tree diagram) has a name (a letter) and an associated size (a number). The
size of leaves may represent for instance the size of individual files, the size of non-leaf
nodes is the sum of the sizes of its children. The treemap is constructed via recursive
subdivision of the initial rectangle. The size of each sub-rectangle corresponds to the
size of the node. The direction of subdivision alternates per level: first horizontally, next
vertically, etcetera. As a result, the initial rectangle is partitioned into smaller rectangles,
such that the size of each rectangle reflects the size of the leaf. The structure of the tree
is also reflected in the treemap, as a result of its construction. Color and annotation can
be used to give extra information about the leaves.
Treemaps are very effective when size is the most important feature to be displayed.
Figure 2(a) shows an overview of a file system: 1400 files are shown and one can effort-
lessly determine which are the largest ones.
However, treemaps have limitations [4]. One problem is that treemaps often fall short
to visualize the structure of the tree. The worst case is a balanced tree, where each parent
Bruls, M., Huizing, K., & van Wijk, J. J. (2000). Squarified Treemaps. In Proc. of Joint Eurographics and IEEE TCVG Symp. on Visualization (TCVG 2000) IEEE Press, pp. 33-42. www.win.tue.nl/~vanwijk/stm.pdf
TREEMAP: ABBILDUNGSVORSCHRIFTEN
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Fig. 4. Subdivision algorithm
These steps are repeated until all rectangles have been processed. Again, an optimal
result can not be guaranteed, and counterexamples can be set up. The order in which the
rectangles are processed is important. We found that a decreasing order usually gives the
best results. The initially large rectangle is then filled in first with the larger subrectan-
gles.
3.2 Algorithm
Following the example, we present our algorithm for the layout of the children in one
rectangle as a recursive procedure squarify. This procedure lays out the rectangles in
horizontal and vertical rows. When a rectangle is processed, a decision is made between
two alternatives. Either the rectangle is added to the current row, or the current row is
fixed and a new row is started in the remaining subrectangle. This decision depends only
on whether adding a rectangle to the row will improve the layout of the current row or
not.
We assume a datatype Rectangle that contains the layout during the computation and
is global to the procedure squarify. It supports a function width() that gives the length of
the shortest side of the remaining subrectangle in which the current row is placed and a
function layoutrow() that adds a new row of children to the rectangle. To keep the de-
scription simple, we use some list notation: ++ is concatenation of lists, is the list
containing element , and is the empty list. The input of squarify() is basically a list
of real numbers, representing the areas of the children to be laid out. The list row con-
Bruls, M., Huizing, K., & van Wijk, J. J. (2000). Squarified Treemaps. In Proc. of Joint Eurographics and IEEE TCVG Symp. on Visualization (TCVG 2000) IEEE Press, pp. 33-42. www.win.tue.nl/~vanwijk/stm.pdf
(a) File system (b) Organization
Fig. 5. Squarified treemaps
(a) File system (b) Organization
Fig. 6. Squarified cushion treemaps
figure 7(a). This method has some disadvantages. Extra screen-space is used, and fur-
thermore, it gives rise to maze-like images, which can be puzzling for the viewer.
However, the second disadvantage can be remedied in a similar way as for the visual-
ization of the nodes. We fill in the borders with grey-shades, based on a simple geometric
model (figure 8). The width in pixels of a border of level , with is given
by:
where is the width of the root level border, and a factor that can be used to decrease
the width for lower level borders. For the profile of the border we use a parabola:
with
TREEMAP: ERGEBNIS
Bruls, M., Huizing, K., & van Wijk, J. J. (2000). Squarified Treemaps. In Proc. of Joint Eurographics and IEEE TCVG Symp. on Visualization (TCVG 2000) IEEE Press, pp. 33-42. www.win.tue.nl/~vanwijk/stm.pdf
NEWSMAP
http://www.marumushi.com/apps/newsmap/newsmap.cfm
MORITZ STEFANER: RELATION BROWSER
CLEMENS LANGO: VISUOS
MORITZ STEFANER: FOLDING TIME
STEFANIE POSAVEC: LITERARY ORGANISM
JONATHAN HARRIS: WE FEEL FINE
JONATHAN HARRIS: THE WHALE HUNT
INDEXED.BLOGSPOT.COM
ROBERT KOSARA: PRESIDENTIAL DEMOGRAPHICS
Jacques Bertin (1967): Sémiologie graphique Engelhardt (2002): The language of graphics
KATHARINA LODERSTAEDT: ONE MINE DAY
JONAS LOH: YOU’RE
JONAS LOH: YOU’RE
VISUALISIERUNG UND INFORMATIONS-AESTHETIK
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Viegas & Wattenberg (2006): Artistic Data Visualization: Beyond Visual Analytics
FLIGHT404: MAGNETIC INK
ANDREAS FISCHER: A WEEK IN THE LIFE
WIRMACHENBUNT.DE: MULTITOUCH PROTOTYPE
VISUAL TOOLS FOR THE SOCIO–SEMANTIC WEB
+ MUCH MORE...
28
http://infosthetics.com
http://flowingdata.com
a) b) c)
Figure 1. The gamut of data-based visualization. a) Parallel Sets [12] show data about the peopleon the Titanic, and are readable and recognizable as a visualization; b) Ambient visualization [18]visualizing a bus schedule are readable but require more effort and are not readily recognizable as avisualization; c) Music visualization like MilkDrop [23] is also based on data, but not readable.
4.1 The Sublime
One aesthetic criterion of particular interest is the sub-lime. The sublime can be understood as that which inspiresawe, grandeur, and evokes a deep emotional and/or intel-lectual response. Works of art generally possess a sublimequality, making them enigmatic and captivating at the sametime. Sack [16] equates its opposite, the anti-sublime, withuser friendliness, which is a central concept in computerscience. In fact, visualization is generally understood to bea part of human-centered computing [11], and techniquesthat are published at the main conferences and in journalsusually need to be evaluated in user studies [13]. They arethus designed to remove any sublimity, and instead fosterimmediate understanding.
While the sublime is just one criterion in aesthetics, itis an incredibly useful one for this discussion. The data-based visualization examples discussed above and shown inFigure 1 can be easily classified using a measure of theirsublimity: while the classical technical information visu-alization is entirely anti-sublime, artistic visualizations areprimarily sublime.
The sublime subsumes the two criteria of readability andrecognizability, since for a work of art to be sublime, it can-not be easily readable (or user friendly). It must presentenough of an enigma to keep an audience interested with-out being easy to solve. The opposite is obviously true fora tool that is designed to aid in data analysis.
4.2 Pragmatic Visualization
Pragmatic visualization is what we term the technicalapplication of visualization techniques to analyze data. Thegoal of pragmatic visualization is to explore, analyze, orpresent information in a way that allows the user to thor-oughly understand the data. Card et al. describe this processas knowledge crystallization [3], and the recent initiatives invisual analytics [19] have used the slogan Detecting the Ex-pected, Discovering the UnexpectedTM.
Visual efficiency is of course a key criterion for work invisualization. The goal is to produce images that convey thedata as quickly and effortlessly as possible. User studies areconducted to measure the speed and accuracy of users, andto compare different methods and tasks [13].
VISUALISIERUNG UND INFORMATIONS-AESTHETIK
Kosara (2006): Visualization Criticism – The Missing Link Between Information Visualization and Art
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VISUALISIERUNG UND INFORMATIONS-AESTHETIK
Lau & Vande Moere (2006): Towards a Model of Information Aesthetics in Information Visualization
DAS NEUE WEB UND
DIE ORDNUNG DER DINGE
ORDNUNG DURCH ÖRTLICHKEIT
ÖRTLICHKEIT + METADATEN
by Flickr user: similarity http://www.flickr.com/photos/reflection/314405398/by Flickr user: leigaaman http://www.flickr.com/photos/kmcarmen1225/133103641/by Flickr user Orin Optiglot http://www.flickr.com/photos/orinrobertjohn/409812627/
DESKTOP METAPHER
SEMANTIC WEB
WEB 2.0
WEB 2.0 Services statt Web–„Seiten”
Fortbewegung → Konversation & Manipulation*
Offene, kollaborative Strukturen
Nutzergenerierte Inhalte
Wisdom of the crowds, Crowdsourcing, Prosumers
*Terry Winograd in B. Moggridge. Designing interactions. The MIT Press, 2006.
WEB 2.0 SERVICES
LIFE LOGGING
THE LONG TAIL
The long tail of web economics (reproduced from [Anderson:2006])
Head
Long!tail
Products
Popularity
VISUAL TOOLS FOR THE SOCIO–SEMANTIC WEB
THE SOCIO–
SEMANTIC
WEB
41
„[A] rich tapestry of words and code that builds on the strange connections between people and content and metadata”
Peter Morville. Ambient findability. O'Reilly, Sebastopol, CA, 2005.
SAY YES
TO THE MESS
Aufräumen können wir ja später.
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ELASTIC TAG MAPS
KOGNITIVE
PERSPEKTIVE
Kategorien
unscharf, subjektiv, Familienähnlichkeit
Klassifikation
Taxonomisch (hierarchisch–enumerativ)
Facettiert (analytico–synthetisch)
Hierarchy Facetted!Classification
* E. K. Jacob. Classification and categorization: a difference that makes a difference. Library Trends, 52(3):515-540, 2004.
WARUM TAGGING
FUNKTIONIERT
* G. Bateson. Mind and nature: a necessary unity (advances in systems theory, complexity, and the human sciences). Hampton Press, Incorporated, 1979.
Kategorien und Eigenschaften anstatt exklusiver
Klassen
Multiple, parallele Dimensionen und Perspektiven
Subjektiv & assoziativ
Nur nach Bedarf
„Umgedrehte Suche”
Hackable
Relevante Information als
„Unterschied, der einen Unterschied macht.”*
WARUM TAGGING
FUNKTIONIERT
Lose, zeitlich dynamische Struktur
Kollaborative Annotation › Intersubjektivität
Effektmultiplikation
Multiple, parallele Dimensionen und Perspektiven
ZEITLICHE DYNAMIK
TOPIC DECAY
Qt(x)
ln(x)
Probability
Time
tagging!event
new!tag
p
tt-1t-2t-3…
1-p
Yule–Simon memory process, adapted from [Cattuto:2006]
EMERGING TOPICS
EXPERIMENT 3: EMERGING TOPICS (HISTOGRAM)
FOLKSONOMIES
INTERSUBJEKTIVITÄT
ELASTIC LISTS FOR FACET BROWSERS
KONDUIT
MACE PROJECT
OAI-PMHHARVESTEDMETADATA
LOM
WINDS
IRB
DYNAMO
ARIADNE
3RD PARTY CONTENT
CONCEPTS
LESSONS
PROJECTS
DESCRIPTIONS
IMAGES
REGULATIONS
3D MODELS
MAPS
CAM–RSS
ATTENTIONMETADATA
CAM
DOMAINMETADATA
COMPETENCEMETADATA
CONTEXT METADATA
WEB SERVICES
FEDERATEDSEARCH
USER MANAGE-MENT
IMPROVED ACCESS
KEYWORDS
architecture design art project inspiration knowledge
amorph Berlin ugly
RenzoPiano
sustainability transparent Venice
toRead cool Bern
architecture design art project inspiration knowledge
amorph Berlin organic
RenzoPiano
sustainability transparent Venice
toRead cool Bern
MAP USERS
Renzo Piano › !nd in MACE
E-LEARNING OBJECTS IMAGES WEB PAGES
› more › more › more
› Contemporary architecture› Renzo Piano Biography› The Paul Klee Museum› eLearning course: Organic shapes› Berlin's architecture in the 1990s
357 results group by type | date | other…
› City of Bern› Renzo Piano building workshop› Paul Klee Museum› Wikipedia: Renzo Piano
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MAP
INTEGRATEDWIDGETS
FACETEDSEARCH
EXPERT INDEXERS
ENRICHED METADATA
ARCHITECTURALCOMMUNITY
AUTOMATICMETHODS
MACE PORTAL
PERSPEKTIVEN Weg von Karten und Graphen
Flach, gewichtet, multi-faceted
Minimale, punktuelle Integration
E.g. Sparklines, Ambient Visualization
Kollaborative Visualisierung
e.g. http://sense.us
Story–telling
VISUAL TOOLS FOR THE SOCIO–SEMANTIC WEB
URLs
68
http://well-formed-data.net
http://well-formed-data.net/thesis
http://mace-project.eu
http://interface.mace-project.eu
VISUAL TOOLS FOR THE SOCIO–SEMANTIC WEB
DANKE
FÜR’S
ZUHÖREN
69