© 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

1-2 © 2010 Pearson Addison-Wesley. All rights reserved. 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. 14-2

1-3 © 2010 Pearson Addison-Wesley. All rights reserved. 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. 14-3

1-4 © 2010 Pearson Addison-Wesley. All rights reserved. Information Visualization Introduction Challenges for Information Visualization Information is Beautiful Interactive Information Visualization of a million Items 14-4

1-5 © 2010 Pearson Addison-Wesley. All rights reserved. Challenges for Information Visualization (cont.) 14-5 Viewing large volumes of data

1-6 © 2010 Pearson Addison-Wesley. All rights reserved. 14-6

1-7 © 2010 Pearson Addison-Wesley. All rights reserved. WorldWideWeb Around Wiki 14-7

1-8 © 2010 Pearson Addison-Wesley. All rights reserved. Data and Images of Taxonomy 14-8 Taxonomy is 1. the science or technique of classification 2. the science dealing with the description, identification, naming, and classification of organisms

1-9 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy 14-9

1-10 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: 1D Linear Data 14-10

1-11 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: 1D Linear Data (cont.) 14-11

1-12 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: 1D Linear Data (cont.) 14-12

1-13 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: 2D Map Data 14-13

1-14 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: 2D Map Data (cont.) 14-14

1-15 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: 3D World Data 14-15

1-16 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: Multidimensional Data 14-16

1-17 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: Multidimensional Data (cont.) 14-17

1-18 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: Temporal Data 14-18

1-19 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: Temporal Data (cont.) 14-19

1-20 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: Tree Data 14-20

1-21 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: Tree Data (cont.) 14-21

1-22 © 2010 Pearson Addison-Wesley. All rights reserved. Data Type by Task Taxonomy: Network Data 14-22

1-23 © 2010 Pearson Addison-Wesley. All rights reserved. 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 14-23

1-24 © 2010 Pearson Addison-Wesley. All rights reserved. Challenges for Information Visualization 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

1-25 © 2010 Pearson Addison-Wesley. All rights reserved. Challenges for Information Visualization (cont.) Combining visual representations with textual labels

1-26 © 2010 Pearson Addison-Wesley. All rights reserved. Challenges for Information Visualization (cont.) Integrating with analytical reasoning techniques