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Slides from resources for:
Designing the User Interface
4th Edition
by Ben Shneiderman & Catherine Plaisant
Slides developed by Roger J. Chapman
Copyright © 2005, Pearson Education, Inc.
Introduction
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Information overload and anxiety common
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Developing more powerful search and visualization methods,
integration of technology with task
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Terms:
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Information gathering
Seeking
Filtering
Visualization
Huge volumes of available data:
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Data mining
Data warehouses and data marts
Knowledge networks or semantic webs
A know-item-search versus making sense and discovering
Copyright © 2005, Pearson Education, Inc.
Introduction
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Traditional interfaces have been difficult for novice users
– Complex commands
– Boolean operators
– Unwieldy concepts
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Traditional interfaces have been inadequate for expert users
– Difficulty in repeating searches across multiple databases
– Weak methods for discovering where to narrow broad searches
– Poor integration with other tools
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Designers are just learning how to present large amounts of data in
orderly and user-controlled ways
Copyright © 2005, Pearson Education, Inc.
Information visualization
• "A picture is worth a thousand words!"
• Large amounts of information in compact and usercontrolled ways
– example: USA map, click a city to see more info
• Information visualization can be defined as the use of
interactive visual representations of abstract data to
amplify cognition
• Scientific visualization
– continuous variables, volumes and surfaces
• Information visualization
– categorical variables and the discovery of patterns, trends,
clusters, outliers, and gaps
Copyright © 2005, Pearson Education, Inc.
Information visualization
• Visual data mining
• Answer questions users didn’t know they had
• Tufte offers advice for static information, but
dynamic displays present a challenge
• Must be more than cool
• The Visual Information Seeking Mantra
– Overview first
– zoom and filter
– then details-on-demand
Copyright © 2005, Pearson Education, Inc.
Copyright © 2005, Pearson Education, Inc.
Examples
• TextArc
• SeeSoft
• Piccolo
Copyright © 2005, Pearson Education, Inc.
Information visualization
• Basic data types
– 1 - Dimensional
• Linear data types include textual documents, program source
code, lists of names in sequential order
• E.g. highlight lines of code that have changed
– 2 - Dimensional
• Planar or map data includes geographic maps, floor plans,
newspaper layouts
• E.g. Geographic Information Systems, spatial displays of
document collections
• Example tasks: find regions containing items
Copyright © 2005, Pearson Education, Inc.
Information visualization
• Basic data types (cont.)
– 3 - Dimensional
• Real-world objects such as molecules, the human body,
buildings
• Users must cope with understanding their position and
orientation when viewing the objects
• E.g. overviews, landmarks, stereo displays, transparency,
color coding
• Virtual Reality displays
• Users’ tasks typically deal with continuous variables
• National Library of Medicine's Visible Human Project
• Controversial
Copyright © 2005, Pearson Education, Inc.
Information visualization
• Basic data types (cont.)
– Multi-Dimensional
• Most relational and statistical databases
• N attributes become points in an n-dimensional
space
• Interface representation could be a 2-D
scattergram with each additional dimension
controlled by a slider
• Parallel coordinate plots
• Table Lens
• Hierarchal or k-means clustering
Copyright © 2005, Pearson Education, Inc.
Information visualization
• Basic data types (cont.)
– Temporal
• Time Lines are widely used and accepted
• Items have a start and finish time and items may overlap
• Tasks include finding all events before, after, or during some time
period
– Tree
• Collections of items with each item having a link to one parent item
(except root)
• Outline style of indented labels or node-and-link diagram
• Space-filling approach
– Networks
• Sometimes data needs to be linked to an arbitrary number of other
items
• Example: A graphical representation of the World Wide Web
• Mode-and-link diagrams, matrices
Copyright © 2005, Pearson Education, Inc.
Information visualization
• Basic tasks
– Overview
• Gain an overview of the entire collection
• Adjoining detail view
• The overview might contain a movable field-of-view box to control
the contents of the detail view
– allowing zoom factors of 3 to 30
• Fisheye view
– Zoom
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Zoom in on items of interest
Allows a more detailed view
Need to maintain context
Particularly important for small displays
– Filter
• Filter out uninteresting items
• Allows user to reduce size of search
Copyright © 2005, Pearson Education, Inc.
Information visualization
• Basic tasks (cont.)
– Details-on-Demand
• Select an item or group and get details when needed
• Useful to pinpoint a good item
• Usually click on an item and review details in a separate or pop-up window
– Relate
• View relationships among items
• Use human perceptual ability – proximity, containment, connected line, color
coding
• Example: Set directors name, and view all movies with that director
– History
• Keep a history to allow undo, replay, and progressive refinement
• Allows a mistake to be undone, or a series of steps to be replayed
– Extract
• Extract the items or data
• Save to file, print, or drag to another application
Copyright © 2005, Pearson Education, Inc.