What is visualisation ? by I42q7B3


									       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
        What are data types ?
• Various different types of data
• Numerical
• Ordinal
  – Naturally order ( days of the week )
• Categorical
  – Not ordered ( animal names )
 Basic Visualization Approaches
 Indentation        Clustering      Node-link diagrams
• Tree control                       • 2D diagrams
                 • Galaxy of News
• Fisheye                            • SemNet
                 • ThemeScape
                                     • Cone Tree
Containment      • Hot Sauce         • Fisheye Cone Tree
• Treemaps
                                     • Hyperbolic viewer
• Pad++                              • FSN
                 • Floor plans       • XML3D
                 • Street maps
     Examples of Visualisation
• London Underground – Harry Beck
• Connectivity
• Deals with connections, not focused on
• Differs from other maps, as familiar
  geography was not overriding concern
London Underground Map 1927
London Underground Map 1990s
        Dr. John Snow:
Statistical Map Visualization


   1855 London Cholera Epidemic
       Visualising Tree Data 1
• CS use of trees for data storage
       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
Tree Maps 1 – Schneiderman
    Tree Maps 2 – Schneiderman
• Johnson &
University of
Maryland, Vis’91

 Space filling
 ~3000 objects

• MicroLogic’s
Hyperbolic Browsing - Lamping
           H3 - 1997

Munzner, Stanford Univ., InfoVis’97
Projected onto sphere: 20,000 nodes
    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
Usefulness of Visualisation in IR
• Allows semantic relationships to be
• Use of Metaphors such as
  – spatial proximity
  – visual links
• Allows users to develop a conceptual map
  of the information space
  Linking IR to real world tasks
• Searching & Browsing of information can
  be related to real world navigation
• Complex Datasets can hide trends /
  – A well design graph can express shopping
    trends through the use of Store Card
         IR and Hypermedia
• WWW – another information space
• Overview Maps & Zooming/Panning
• Improve performance and satisfaction
• Move ‘load’ from cognitive to perceptual
• visualising and directly interact with
  conventional hypermedia and unstructured
    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
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
• Adorned (color, texture)

                      Reference: Noik (Graphics Interface’94)
            Approaches to IV
•    Core approaches - Colebourne et al.
1.   'Benediktine' cyberspace
2.   statistical clustering and proximity
3.   hyper-structures
4.   human centred
•    Categories are not mutually exclusive
       '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
'Benediktine' cyberspace
     Statistical Clustering and
• Applies statistical models to data prior to
  presenting the visualisation
• conveys spatially the underlying semantic
• spatial proximity of documents -> reflect
  their semantic similarity
• Various techniques generate these semantic
  proximities (eg Vector Space Model)
Statistical Clustering and
• extend the notion of hypertext directly
• use 3-D graph drawing algorithms to create
  the visualisation
• Works well where explicit links exist, eg in
• Various graph visualisation techniques
    Hyper-structure (Cone Tree 1)

 Mackinlay &
 Card, Xerox

 10 levels
 1000 nodes
 Up to 10,000
Hyper-structure (Cone Tree 2)
            Human centred
• Two main areas
1. Exploit the user's real world experience,
   by representing information spaces using
   real world metaphors
2. Allow the user themselves to organise the
   information in a manner that they find
Human centred – Exploit user
 Human centred – User
themselves organise data
   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
   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
    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
• Relate: relationship between items
• History & Extract
    Combining Sound & Visual
• 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)
           Further Readings
• Chen, C. (1999) Information Visualisation
  and Virtual Environments
• Card, S et al (1999) Readings in
  Information Visualization: Using Vision to
• Spence, R. (2001) Information
• http://www.cribbin.co.uk/infovis.htm

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