The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Ben Shneiderman, 1996

Transcription

1 The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations Ben Shneiderman, 1996

2 Background the growth of computing + graphic user interface 1987 scientific visualization 1989 information visualization more and more data types and tasks order terms information retrieval (in bibliographic systems) database management (in structured database systems) newer notions information gathering, seeking, visualization data mining, warehousing, filtering Pei Shan Tsai 1 Introduction 2

3 Information Exploration Goals known item search find a set of items that satisfy a well-understood information need browse developing an understanding of unexpected patterns Pei Shan Tsai 1 Introduction 3

4 Information Exploration Challenges the growth of data size data overload problem how to present data in orderly provide interaction with user Pei Shan Tsai 1 Introduction 4

5 Data Presentation Design utilize humans perceptual abilities visual perceptual ability is greater than the other senses data visualization Pei Shan Tsai 2 Visual Information Seeking Mantra 5

6 Data Visualization scientific visualization make 3d phenomena visible and comprehensible e.g. heat conduction, airflow information visualization reveal patterns, clusters, gaps, or outliers in abstract data e.g. stock market trades, document collection Pei Shan Tsai 2 Visual Information Seeking Mantra 6

7 Visual Information Seeking Mantra overview first, zoom and filter, then details-on-demand basic visual design principle a starting point in trying to characterize information visualizations Pei Shan Tsai 2 Visual Information Seeking Mantra 7

8 Task by Data Type Taxonomy 7 data types organized by the problems users are trying to solve 1d, 2d, 3d, temporal, multi-dimensional, tree, network data 7 tasks actions that users wish to perform overview, zoom, filter, details-on-demand, relate, history, extract Pei Shan Tsai 3 Task by Data Type Taxonomy 8

9 1d Data e.g. textual document, source code, alphabetical list features linear data items are organized in a sequential manner the problems users are trying to solve basic tasks count, search, select, etc Pei Shan Tsai 3 Task by Data Type Taxonomy 9

10 1d Data Visualization Example bifocal display (Spence and Apperley, 1982) detailed information in the focus area less information in the surrounding area Figure 1: an information space Figure 2: wrap around two uprights Figure 3: view from an appropriate direction Pei Shan Tsai 3 Task by Data Type Taxonomy 10

11 1d Data Visualization Example SeeSoft (Eick et al., 1992) Figure 1: SeeSoft shows locations of characters within a text Pei Shan Tsai 1 Introduction 11

12 2d Data e.g. geographic map, floorplan, newspaper layout features planar data items cover some part of the total area items may be rectangular or not the problems users are trying to solve deal with 2d space relationship between items up/down, left/right, inside/outside basic tasks Pei Shan Tsai 3 Task by Data Type Taxonomy 12

13 2d Data Visualization Example multilayer approach Figure 1: a geographic information systems as a layered cake Pei Shan Tsai 3 Task by Data Type Taxonomy 13

14 3d Data e.g. molecule, human body, building features real world objects items with volume items may have complex relationship between each other the problems users are trying to solve deal with 3d space relationship between items up/down, left/right, front/back, inside/outside basic tasks Pei Shan Tsai 3 Task by Data Type Taxonomy 14

15 3d Data Visualization Example visible human project (North et al., 1996) Figure 1: a poster of National Library of Medicine s Visible Human Project Pei Shan Tsai 3 Task by Data Type Taxonomy 15

16 Temporal Data e.g. medical history, project management, video features time lines items have a begin and end time items may overlap the problems users are trying to solve deal with time at, before, after some moment during some time period basic tasks Pei Shan Tsai 3 Task by Data Type Taxonomy 16

17 Temporal Data Visualization Example perspective wall (Robertson et al., 1993) Figure 1: perspective wall with the bifocal display Pei Shan Tsai 3 Task by Data Type Taxonomy 17

18 Temporal Data Visualization Example LifeLines (Plaisant et al., 1996) Figure 1: LifeLines shows personal medical history Pei Shan Tsai 3 Task by Data Type Taxonomy 18

19 Multi-dimensional Data e.g. most relational databases, statistical databases features items with n attributes is in a n-dimensional space the problems users are trying to solve deal with relationship between items patterns, clusters, gaps, outliers deal with relationship between attributes correlation, dependence basic tasks Pei Shan Tsai 3 Task by Data Type Taxonomy 19

20 Multi-dimensional Data Visualization Example parallel coordinates (Inselberg, 1985) Figure 1: parallel coordinates for iris dataset Pei Shan Tsai 3 Task by Data Type Taxonomy 20

21 Tree Data e.g. computer directory file management features hierarchy items have a link to one parent item, except the root links may have attributes the problems users are trying to solve deal with tree structural properties depth, degree, path, etc. basic tasks Pei Shan Tsai 3 Task by Data Type Taxonomy 21

22 Tree Data Visualization Example indented outlines (Egan et al., 1989) Figure 1: varied indented outline styles Pei Shan Tsai 3 Task by Data Type Taxonomy 22

23 Tree Data Visualization Example cone trees (Robertson et al., 1993) Figure 1: cone tree Pei Shan Tsai 3 Task by Data Type Taxonomy 23

24 Network Data e.g. World Wide Web features items have links to an arbitrary number of other items links may have attributes the problems users are trying to solve deal with network structural properties cycle, shortest path, etc. basic tasks Pei Shan Tsai 3 Task by Data Type Taxonomy 24

25 Network Data Visualization Example Netmap Figure 1: Netmap layout of nodes on a circle with links crisscrossing the central area Pei Shan Tsai 3 Task by Data Type Taxonomy 25

26 Task by Data Type Taxonomy 7 data types organized by the problems users are trying to solve 1d, 2d, 3d, temporal, multi-dimensional, tree, network data 7 tasks actions that users wish to perform overview, zoom, filter, details-on-demand, relate, history, extract Pei Shan Tsai 3 Task by Data Type Taxonomy 26

27 Overview gain an overview of the entire collection pan or scroll through the collection Figure 1: pan icons Figure 2: scroll icon Pei Shan Tsai 3 Task by Data Type Taxonomy 27

28 Zoom zoom in on items of interest control zoom focus and zoom factor Figure 1: zoom in icon Figure 2: zoom out icon Pei Shan Tsai 3 Task by Data Type Taxonomy 28

29 Overview and Zoom Strategy both change views of data overview plus detail view / context plus focus view field-of-view box Figure 1: field-of-view box with the bifocal display Pei Shan Tsai 3 Task by Data Type Taxonomy 29

30 Overview and Zoom Strategy both change views of data overview plus detail view / context plus focus view fisheye (Furnas, 1986) Figure 1: fisheye with the bifocal display Pei Shan Tsai 3 Task by Data Type Taxonomy 30

31 Filter filter out uninteresting items apply dynamic queries to items to control the contents of display rapid display update < 100 milliseconds Pei Shan Tsai 3 Task by Data Type Taxonomy 31

32 Details-on-demand select an item or group and get details when need click on to get a popup window with details Pei Shan Tsai 3 Task by Data Type Taxonomy 32

33 Relate view relationships among items highlight items that have similar attributes to the selected one Pei Shan Tsai 3 Task by Data Type Taxonomy 33

34 History keep a history of actions to support undo, replay, and progressive refinement since information exploration is rarely finished in a single step keeping the history of users actions for retracing Figure 1: undo icon Figure 2: redo icon Pei Shan Tsai 3 Task by Data Type Taxonomy 34

35 Extract allow extraction of sub-collections and of the query parameters extract the desired items for further use save, send, print, etc Pei Shan Tsai 3 Task by Data Type Taxonomy 35

36 Dynamic Query direct-manipulation query the use of rapid, incremental, and reversible actions sliders, buttons the immediate display of feedback Boolean expressions OR within an attribute AND of attributes across attributes (attr1.qry1 attr1.qry2) (attr2.qry1 attr2.qry2) Pei Shan Tsai 4 Advanced Filtering 36

37 Boolean Expressions Visualization Venn diagrams (Michard, 1982), decision tables (Greene et al., 1990) become confusing as query complexity increases filter-flow model the metaphor of water flowing flow from left to right through a series of pipes and filters Figure 1: Venn diagrams Figure 2: decision table Pei Shan Tsai 4 Advanced Filtering 37

38 Filter-flow Model AND a linear sequence of filters OR within an attribute, a single filter across multiple attributes, parallel filters NOT an inverter in filters Figure 1: (x > 7) (x > 9) Figure 2: (x < 7) (x > 9) Figure 3: (x > 9) (y > 9) Figure 4: (x > 7) Pei Shan Tsai 4 Advanced Filtering 38

39 Summary while research prototypes typically deal with only one data type, commercial products will have to accommodate several and support full tasks although the computer contributes to the information explosion, it also provide powerful capabilities to deal with dynamic queries and data visualization Pei Shan Tsai 5 Summary 39