What is visualisation

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What is visualisation ?

• Visualise (vb) to form a mental image or vision
  of
• Cognitive ability
• Allows us to internalise data
• Gain insight and understanding
• Internal Map Cognitive Model

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What are data types ?

• Various different types of data
• Numerical
• Ordinal
• Naturally order ( days of the week )
• Categorical
• Not ordered ( animal names )

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Basic Visualization Approaches

• Clustering
• Galaxy of News
• ThemeScape
• Hot Sauce
• Geographic
• Floor plans
• Street maps

• Node-link diagrams
• 2D diagrams
• SemNet
• Cone Tree
• Fisheye Cone Tree
• Hyperbolic viewer
• FSN
• XML3D

• Indentation
• Tree control
• Fisheye
• Containment
• Treemaps
• Pad

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Examples of Visualisation

• London Underground Harry Beck
• Connectivity
• Deals with connections, not focused on geography
• Differs from other maps, as familiar geography
  was not overriding concern

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London Underground Map 1927
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London Underground Map 1990s
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Dr. John SnowStatistical Map Visualization
Broad StreetPump

• 1855 London Cholera Epidemic

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Visualising Tree Data 1

• CS use of trees for data storage

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Visualising Tree Data 2

• Difficult to visualise large tree structures
• Take a company
• CEO as the root node
• People reporting to him at next level
• So on until all the employees are included

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Tree Maps 1 Schneiderman
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Tree Maps 2 Schneiderman

• Johnson Schneiderman, University of Maryland,
  Vis91
• Space filling
• 3000 objects
• MicroLogics DiskMapper

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Hyperbolic Browsing - Lamping
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H3 - 1997
Munzner, Stanford Univ., InfoVis97 Projected
onto sphere 20,000 nodes
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Information Visualisation in Information Retrieval

• on-line information
• diversity of users of such resources
• potential overload
• establish new formats for the presentation and
  manipulation of electronic data
• spatial ability is an important predictor of
  effectiveness and efficiency when performing
  common information (i.e. textual) search tasks

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Usefulness of Visualisation in IR

• Allows semantic relationships to be represented
• Use of Metaphors such as
• spatial proximity
• visual links
• Allows users to develop a conceptual map of the
  information space

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Linking IR to real world tasks

• Searching Browsing of information can be
  related to real world navigation
• Complex Datasets can hide trends / information
• A well design graph can express shopping trends
  through the use of Store Card information

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IR and Hypermedia

• WWW another information space
• Overview Maps Zooming/Panning
• Improve performance and satisfaction
• Move load from cognitive to perceptual
  processes
• visualising and directly interact with
  conventional hypermedia and unstructured text

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Combing IR and VR new perceptions of data

• Virtual Reality (VR) environments can further
  enhance visualisations
• Allows for
• Real Time Interactivity
• Viewing of relationships between object from
  unlimited number of perspectives
• Can allow for haptic or non-visual methods of
  feedback to the user

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Visualization Taxonomy - 1994

• Implicit (use of perspective)
• Continuous focus and context
• Filtered (removing items of low interest)
• Discrete focus and context
• Distorted (size, shape, position of elements)
• Adorned (color, texture)

Reference Noik (Graphics Interface94)
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Approaches to IV

• Core approaches - Colebourne et al. (1994)
• 'Benediktine' cyberspace
• statistical clustering and proximity
• hyper-structures
• human centred
• Categories are not mutually exclusive

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'Benediktine' cyberspace

• Benedikt - 1991
• assigns object attributes (e.g. file size, age,
  key words) on to extrinsic (x,y,z) and intrinsic
  (e.g. shape) dimensions.
• Well suited to data that is explicitly structured
  

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'Benediktine' cyberspace
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Statistical Clustering and Proximity

• Applies statistical models to data prior to
  presenting the visualisation
• conveys spatially the underlying semantic
  structure.
• spatial proximity of documents -gt reflect their
  semantic similarity
• Various techniques generate these semantic
  proximities (eg Vector Space Model)

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Statistical Clustering and Proximity
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Hyper-structures

• extend the notion of hypertext directly
• use 3-D graph drawing algorithms to create the
  visualisation
• Works well where explicit links exist, eg in
  hypertext
• Various graph visualisation techniques available

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Hyper-structure (Cone Tree 1)
Robertson, Mackinlay Card, Xerox PARC,
CHI91 Limits 10 levels 1000 nodes Up to 10,000
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Hyper-structure (Cone Tree 2)
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Human centred

• Two main areas
• Exploit the user's real world experience, by
  representing information spaces using real world
  metaphors
• Allow the user themselves to organise the
  information in a manner that they find intuitive

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Human centred Exploit user experience
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Human centred User themselves organise data
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Visual Information Seeking 1

• Research by Ben Schneiderman
• Direct-manipulation interfaces
• Certain tasks a visual presentation is much
  easier to comprehend than text
• Mantra Overview first, zoom and filter, then
  details on demand

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Visual Information Seeking 2

• Schneiderman 7 Data Types
• 1-, 2-, 3-d data, temporal, multi-dimensional,
  tree and network data
• All items have attributes and simple search task
  is to find all items which a certain set of
  attributes

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Visual Information Seeking 3

• Overview of a collection
• Zoom on items of interest
• Filter out uninteresting items
• Details-on-Demand of a item or group of items
• Relate relationship between items
• History Extract

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Combining Sound Visual retrieval

• Aural presentation contains addition information
  not found in visual representations
• Omni directional information
• Encoding of information, multiple streams
• Cocktail Party Effect - Arons 1992
• Recognition of sounds, is most often sufficient
  to hear only 500 ms to 2 seconds of the
  characteristic or significant part of a sound
  (Warren 1999)

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Further Readings

• Chen, C. (1999) Information Visualisation and
  Virtual Environments
• Card, S et al (1999) Readings in Information
  Visualization Using Vision to Think
• Spence, R. (2001) Information Visualization
• http//www.cribbin.co.uk/infovis.htm