Interaction. CS Information Visualization. Chris Plaue Some Content from John Stasko s CS7450 Spring 2006

Transcription

1 Interaction CS Information Visualization Chris Plaue Some Content from John Stasko s CS7450 Spring 2006

2 Hello. What is this?! Hand back HW! InfoVis Music Video! Interaction Lecture remindme.mov (46.3 MB) 2

3 Academia Degrees of Separation 3

4 InfoVis Components Representation What are we showing? Challenging to be creative Interaction How do we go about showing it? This is where the action is! How do users interact with views? Distinguishes from static vis on paper Question: How do you go about analyzing something? 4

5 Science of Interaction! Illuminating the Path book lays out an agenda for visual analytics. There is a call for a science of interaction where we need to understand interaction better; what does it provide to the interface and what are the possibilities 5

6 What is Interaction? 6

7 What is Interaction?! Let s define interactive Influencing or having an effect on each other.! Response time are important.1 second " Animation, visual continuity, sliders 1 second " System response, conversation break 10 seconds " Cognitive response 7

8 Interaction Web Example Source: 8

9 Interaction Web Example Source: 9

10 What are some types of interaction? 10

11 Classic Interaction Types Dix & Ellis (AVI 1998)! Highlighting & focus! More info! Overview & context (zooming and fisheyes)! Same representation, different parameters! Linking representations (temporal fusion) Keim (TVCG 2002)! Projection! Filtering! Zooming! Distortion! Linking! Brushing 11

12 Specific Techniques! Selection Using an input device to select and identify an element Drill-down for more details Example: Digital History! Pop-up tooltips Hovering a mouse cursor brings up the details of an item The data view is not changed drastically 12

13 Labeling! Challenges Want labels to be readable Obscurity! Check out Fekete & Plaisant CHI 99 for taxonomy of labeling 13

14 Excentric Labels! A dynamic technique to label a neighborhood of objects located around the cursor! Goals: Readable, non-ambiguous, do not hide important information! Web Demo excentric/#prototypes 14

15 Technique: Details-on-Demand! Provide a viewer with more information and details about a data case(s). Want more information about a specific case Want to change from an aggregation view to an individual view " Scaling of data may result in not all shown " Data may be abstracted 15

16 Specifics: Direct Walk! A link exists between cases! Supports active exploring! Examples? Specifics: Rearrange View! Rearrange elements in a view, but keep same data shown Sorting Why? 16

17 Rearrange New perspective Limited real estate TableLens 17

18 Changing Representation 18

19 Highlighting! Viewer may wish to examine different attributes of a data case simultaneously! Or, viewer may wish to view data case under different perspective or representations?! What s the challenge here? 19

20 Brushing! Applies when you have multiple views of the same data! Select one case in a view and the same case is highlighted in the other views 20

21 Filtering & Limiting! Fundamental interactive operation of InfoVis.! Change the set of data case being presented by focusing, narrowing/widening. Zooming & Panning! Many InfoVis systems provide these capabilities on display Pure geometric zoom, semantic zoom More on this later in the course 21

22 Dynamic Query! Best-known and very useful technique! Example: Purchase a house/condo in ATL How do you find out what s on the market? Select house-address From atl-realty-db Where price >= 200,000 and price <= 400,000 and bathrooms >= 3 and garage == 2 and bedrooms >= 4 Database Query 22

23 Database Queries Pros? Cons? Results Found: 1,349 matches or 0 23

24 Database Queries Pros? 1. Powerful Cons? 1. Need to learn language 2. Shows exact matches 3. Don t know the magnitude of the results 4. Context is missing Results Found: 1,349 matches or 0 24

25 Solution: Dynamic Queries! Specifying a query brings immediate display of results.! Responsive interaction (<.1 second) with data and presentation of solution! Fly through the data, promote exploration, and the experience Timesharing vs. batch 25

26 Dynamic Query Properties! Visual representation of world of action including both the objects and actions! Rapid, incremental, and reversible actions! Selection by pointing (not typing)! Immediate and continuous display of results Shneiderman (IEEE Software 94) Ahlberg & Shneiderman (CHI 04) 26

27 Why are DQ a good fit?! What kind of world do we live in? 27

28 Why are DQ a good fit?! What kind of world do we live in? Imperfection: There often isn t one perfect response to a query. Want to understand a set of tradeoffs and choose some best compromise You can also learn more about your problem as your explore 28

29 Video Example: DQ homefinder.mp4 (70.3MB) University of Maryland: HomeFinder 29

30 Web Example: HouseFinder 30

31 Video: FilmFinder (4:12) 1996_filmfinder.mpg (70.3 MB) 31

32 Recap! What were commonalities in these examples? 32

33 Query Controls! Variable types Binary nominal - Buttons Nominal w/ low cardinality - Radio Btn Ordinal, quantitative - Sliders 33

34 Goldfinger A B C DEF G HI J K L M N OP QR S T UVW XYZ Current selection Slider thumb Slider area Index Low selection thumb Real data range High selection thumb 34

35 Commercial Application! Spotfire 35

36 Spotfire! Starfield display! Tight coupling Features to guide the user Rapid, incremental, reversible interactions Display invariants Continuous display Progressive refinement Details on demand 36

37 Web Example 37

38 Dynamic Queries Pros? Cons? 38

39 Dynamic Queries Pros? 1. Work is faster 2. Reversing, undo -> exploration 3. Natural interaction 4. Shows the data Cons? 1. Ops are fundamentally conjective 2. Can you formulate an arbitrary boolean expression? 3. Controls are global in scope 4. Controls are fixed in advance 5. Controls take space (Fix!) 39

40 Data Visualization Sliders! Put data in the controls Low selection thumb High selection thumb Data distribution Eick, UIST 94 40

41 Other Dynamic Query Cons! As data set gets larger, real-time interaction becomes increasingly difficult! What data structures do we use for storage? Linear array Grid file Quad, k-d trees Bit vectors 41

42 Brushing Histograms! Special case of brushing! Data values are represented in histograms that can be clicked on and selected! When items are selected, the corresponding item(s) are highlighted in the main view windows 42

43 Attribute Explorer Video: attribute.mov (36.9 MB) Spence, R., and Tweedie, L. The Attribute Explorer: Information Synthesis via Exploration. Interacting with Computers, 1996! Show all the data, using different displays for different variables! Use brushing so that interaction with one view highlights selections in others! Useful in zero-hit situations or when you re not familiar with the data 43

44 Example (but no demo) DataMaps Maryland & VaTech 44

45 Dynamic Query vs. Brushing Histograms! Empirical Study (Li & North, InfoVis 03) Use DataMaps, a visualization tool for US geographic data. Have participants do tasks with both methods " How many states have population between x and y in 1970? " Given 3 states, which has the lowest median income? " What kind of a state is Florida 45

46 Results! Brushing histograms resulted in better performance.! More highly rated for complex discovery tasks Attribute correlation, compare, and trend evaluation! Dynamic queries are better for more simple range specification tasks Single range, multiple ranges, multiple criteria 46

47 Fundamental Differences Brushing Histograms Dynamic Query Highlights data of interest Allows multiple range queries Filters unwanted data Does single range query Users interact directly with data Users interact with the query (low/high) Displays query results (I/O) Visualizes query formulation (1 way) 47

48 Video: magiclenses.mp4 (65 MB) Magic Lens! Allow interaction with a more focused region of data! More direct-manipulationish! Interaction occurs on top of data! Arbitrary-shaped (usually rectangular) region with some operation that changes the user s view of data Moveable, stackable, parameters 48

49 Pros / Cons of Magic Lens Liveness Flexibility Advantages Can specify complex queries Don t need as much screen real-estate for controls Disadvantages More complex than DQ sliders As a result, not as easy to learn More difficult to implement 49

50 Pros / Cons of Magic Lens Liveness Flexibility Advantages Can specify complex queries Don t need as much screen real-estate for controls Disadvantages More complex than DQ sliders As a result, not as easy to learn More difficult to implement 50

51 Dust & Magnet! InfoVis via a magnet metaphor where magnets represent attributes and attract dust representing data cases. Video: dust-magnet.mov (36.9MB) But you need Quicktime 7ish for it! So, or search for Dust and Magnet on it Yi et al (InfoVis 05) 51

52 Assignment: Yi et al 52

53 Assignment: Yi et al! Maybe interaction and representation aren t so mutually exclusive! What is interaction? Passive interaction (Spence) Menu activities In InfoVis, more designed for changing and adjusting visual representations than for entering data. Same goal, different interaction technique.! Survey of 59 papers, 51 systems, and 311 interaction techniques. 53

54 The Big 7 (1 & 2)! Select: ability to mark a data item of interest to keep track of it. Example: Google Earth Not a standalone technique! Explore: examine a different subset of data cases Example: panning in any system, direct-walk 54

55 The Big 7 (3 & 4)! Reconfigure: give users a different perspective onto data set Example: sort, rearrange in TableLens Important to remove occlusions! Encode: alter the visual representation of data (i.e. appearance) elements Example: color, size, orientation, font. 55

56 The Big 7 (5 thru 7)! Abstract/Elaborate: adjust the levels of details of a data representation Example: digging in TreeMaps! Filter: change the set of data items presented via some conditions Example: Dynamic queries! Connect: show relationships (including those that aren t obvious) 56

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63 That s it!! Next lecture: Time-series data 63