1 Dimensions / Depth James Slack CPSC 533C February 10, 2003

2 Overview Linear data sources Information processing Aggregate visualization methods Embedding semantics of information Repetition and other patterns Examples in InfoVis

3 Linear Data Sources Univariate data arranged spatially or temporally Complexity issues: –Patterns in text are cognitively hard to find –Text input could be viewed spatially –Cognition from visual abstractions of text is becoming more relevant

4 Information Processing Why do we need information? Technical aspects Characterizing text by language semantics Browsing versus querying Interfacing with text visualization

5 Considering Visualization? The technical considerations: 1.Define what needs to be visualized 2.Transform input; must be possible! 3.Analyze to suit the input 4.Technique & derivative data storage

6 Text Features 3 general types of features 1.Frequency based 2.Statistics on words or other tokens 3.Semantic features

7 Text Features Frequency based text features: –Statistics on presence and count of unique words –Feature sets are word statistics

8 Text Features Statistics on words or other tokens –Occurrence, frequency, and context of individual tokens define feature set –Sets can be explicitly specified or deterministically partitioned

9 Text Features Semantic features –Natural groups of similar topics –Knowledge of language –Words have semantic meaning

10 Characterizing Text Feature sets of text –A shorthand description of the original –Reduction in length, not in meaning –Semantics are often important, although not always necessary –Represented for efficient computation

11 Browsing vs. Querying Querying is more precise –Specific results discarded or retained –The most specific features are important –Popularity of query is relative, closeness ratio compares potential matches –Similarity of results appear

12 Browsing vs. Querying Browsing is more general –Choose similarity over exactness –The most common features are important –Clustering is a natural partition –Similarity of clusters appears –Analytical information processing

13 Interfacing With Visualizations Spatial representations enhance cognition Clusters can be viewed with browsing A global overview of data is important Techniques to visit clusters Too many data points? –Display cluster centroids instead

14 Assisting Perception Interface should provide: 1.Preconscious visual form for information 2.Interactions to sustain, enrich process of knowledge building 3.Fluid environment for reflective cognition 4.Framework for temporal knowledge building

15 Aggregate Visualization Information overloads cognitive abilities Understanding global, not local contexts Visualize abstract representations of complex underlying structure What can we gain from global context?

16 Embedding Semantics Are some visualizations without meaning? Galaxies, ThemeScapes highlight semantic meaning with relevant labels Cluster viewer uses calendar to highlight temporal univariate patterns Dot plots, arc diagrams use connectivity of similar input strings independent of semantics

17 Repetition and Patterns How can you show something is repeated? –Place two occurrences close together –Colour two occurrences similarly –Connect two occurrences with a line Each method has merits –No method works in all cases –We want to keep spatial/temporal information

18 Infovis Examples SPIRE –Galaxies and ThemeScapes Calendar Based Visualization Dot Plots Arc Diagrams

19 From SPIRE Spatial Paradigm for Information Retrieval and Exploration Galaxies cluster docupoints ThemeScapes model landscape

20 Galaxies Projection of clustering algorithms into 2D Galaxies are clusters of related data Proximity of galaxies is relevant Designed to add temporal patterns to clustering

21 Galaxies

22 ThemeScape Abstract 3D landscape of information Reduce cognitive load using terrain Elevation, colour encode theme strength redundantly Landscape metaphor translates well –Peaks are easy to recognize –Interesting characteristics include ridges and valleys

23 ThemeScape

24 ThemeScape

25 Calendar Based Visualization Time is linear, monotonic, scalar Prediction is a useful side effect of visualizing the past Time series data is often univariate Periodic patterns emerge in time series data

26 Calendar Based Visualization How about using 3 dimensions? –X-axis: Time of day –Y-axis: Days of data period –Z-axis: Univariate data samples

27 Calendar Based Visualization

28 Calendar Based Visualization Weekly variation obscured by pretty graphics Where are the trends? Is colour necessary for this? Is colour sufficient for this? Can everything be shown without overload?

29 Calendar Based Visualization A more natural way: use a calendar Cluster data into meaningful groups –Decide what the groups mean later? 1.Simple formulae are sufficient for clustering 2.Use robust statistical techniques 3.Generate binary clustering trees 4.Select desired clusters to visualize 5.Show clusters on calendar layout, simple graphs coloured appropriately

30 Calendar Based Visualization

31 Visualizing Structure in Strings M. Wattenberg: Arc diagrams Summarize long strings, indicate repetition

32 Dot Plots Finds structure in string data Correlation matrix Diagonal symmetry Redundant information Interesting repetitions can be confusing

33 Dot Plots

34 Arc Diagrams Finds structure in string data Cognitive improvement over dot plots Adaptable to reduce noise in data Applications are varied: –Music –Text –Compiled code –Nucleotide sequences

35 Arc Diagrams Interactive demonstration: –

36 Alternate Ending Something went wrong with the demo, so here is a synopsis of arc diagrams

37 Arc Diagrams

38 Arc Diagrams

39 Arc Diagrams

40 Arc Diagrams

41 Paper References Visualizing the non-visual: spatial analysis and interaction with information from text documents Wise, J.A.; Thomas, J.J.; Pennock, K.; Lantrip, D.; Pottier, M.; Schur, A.; Crow, V., Proc InfoVis 1995.Visualizing the non-visual: spatial analysis and interaction with information from text documents Cluster and Calendar based Visualization of Time Series Data Jarke J. van Wijk Edward R. van Selow, Proc InfoVis 99.Cluster and Calendar based Visualization of Time Series Data Arc Diagrams: Visualizing Structure in Strings. Martin Wattenberg, Proc InfoVis 2002.Arc Diagrams: Visualizing Structure in Strings