Dimensions / Depth

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Dimensions / Depth

James SlackCPSC 533C

February 10, 2003

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Overview

• Linear data sources

• Information processing

• Aggregate visualization methods

• Embedding semantics of information

• Repetition and other patterns

• Examples in InfoVis

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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

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

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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

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Text Features• 3 general types of features

1.Frequency based

2.Statistics on words or other tokens

3.Semantic features

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Text Features• Frequency based text features:

– Statistics on presence and count of unique words

– Feature sets are word statistics

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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

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Text Features• Semantic features

– Natural groups of similar topics– Knowledge of language– Words have semantic meaning

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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

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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

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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

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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

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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

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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?

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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

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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

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Infovis Examples• SPIRE

– Galaxies and ThemeScapes• Calendar Based Visualization• Dot Plots• Arc Diagrams

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From SPIRE• Spatial Paradigm for Information Retrieval

and Exploration• Galaxies cluster docupoints• ThemeScapes model landscape

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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

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Galaxies

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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

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ThemeScape

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ThemeScape

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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

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Calendar Based Visualization• How about using 3 dimensions?

– X-axis: Time of day– Y-axis: Days of data period– Z-axis: Univariate data samples

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Calendar Based Visualization

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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?

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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

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Calendar Based Visualization

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

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

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Dot Plots

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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

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Arc Diagrams• Interactive demonstration:

– http://www.turbulence.org/Works/song/mono.html

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Alternate Ending• Something went wrong with the demo, so

here is a synopsis of arc diagrams

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Arc Diagrams

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Arc Diagrams

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Arc Diagrams

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Arc Diagrams

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Paper References• Visualizing the non-visual: spatial analysis and interaction with i

nformation from text documents Wise, J.A.; Thomas, J.J.; Pennock, K.; Lantrip, D.; Pottier, M.;

Schur, A.; Crow, V., Proc InfoVis 1995. • Cluster and Calendar based Visualization of Time Series Data

Jarke J. van Wijk Edward R. van Selow, Proc InfoVis 99. • Arc Diagrams: Visualizing Structure in Strings. Martin

Wattenberg, Proc InfoVis 2002.