Chap14

© 2010 Pearson Addison-Wesley. All rights reserved.

Addison Wesley

is an imprint of

Designing the User Interface:

Strategies for Effective Human-Computer Interaction

Fifth Edition

Ben Shneiderman & Catherine Plaisant

in collaboration with

Maxine S. Cohen and Steven M. Jacobs

CHAPTER 14:

Information Visualization

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Information Visualization

• Introduction

• Data Type by Task Taxonomy

• Challenges for Information Visualization

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Introduction

• “A Picture is worth a thousand words”

• Information visualization can be defined as the use of interactive visual representations of abstract data to amplify cognition (Ware, 2008; Card et al., 1999).

• The abstract characteristic of the data is what distinguishes information visualization from scientific visualization.

• Information visualization: categorical variables and the discovery of patterns, trends, clusters, outliers, and gaps

• Scientific visualization: continuous variables, volumes and surfaces

• Information visualization provides compact graphical presentations and user interfaces for interactively manipulating large numbers of items, possibly extracted from far larger datasets.

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Introduction (cont.)

• Sometimes called visual data mining, it uses the enormous visual bandwidth and the remarkable human perceptual system to enable users to make discoveries, take decisions, or propose explanations about patterns, groups of items, or individual items.

• Visual-information-seeking mantra:

- Overview first, zoom and filter, then details on demand.





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Data Type by Task Taxonomy

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Data Type by Task Taxonomy: 1D Linear Data

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(cont.)

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(cont.)

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Data Type by Task Taxonomy: 2D Map Data

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Data Type by Task Taxonomy: 2D Map Data

(cont.)

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Data Type by Task Taxonomy: 3D World Data

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Data Type by Task Taxonomy:

Multidimensional Data

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Data Type by Task Taxonomy:

Multidimensional Data (cont.)

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Data Type by Task Taxonomy: Temporal Data

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Data Type by Task Taxonomy: Temporal Data

(cont.)

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Data Type by Task Taxonomy: Tree Data

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Data Type by Task Taxonomy: Tree Data (cont.)

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Data Type by Task Taxonomy: Network Data

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The seven basic tasks

1. Overview task - users can gain an overview of the entire collection

2. Zoom task - users can zoom in on items of interest

3. Filter task - users can filter out uninteresting items

4. Details-on-demand task - users can select an item or group to get details

5. Relate task - users can relate items or groups within the collection

6. History task - users can keep a history of actions to support undo, replay, and progressive refinement

7. Extract task - users can allow extraction of sub-collections and of the query parameters

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Challenges for Information Visualization

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• Importing and cleaning data

• Combining visual representations with textual labels

• Finding related information

• Viewing large volumes of data

• Integrating data mining

• Integrating with analytical reasoning techniques

• Collaborating with others

• Achieving universal usability

• Evaluation

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(cont.)

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• Combining visual representations with textual labels

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(cont.)

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• Viewing large volumes of data

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(cont.)

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• Integrating with analytical reasoning techniques

Chap14

Technology

Transcript of Chap14