Visual Analytics Research at WPI

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Visual Analytics Research at WPI

• Dr. Matthew Ward and Dr. Elke Rundensteiner
• Computer Science Department

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What is Visual Analytics?

• The science of analytical reasoning facilitated
  by interactive visual interfaces, from
  Illuminating the Path the Research and
  Development Agenda for Visual Analytics, J.
  Thomas and K. Cook (eds.), 2005
• More than information visualization or visual
  data mining, it involves technology to support
  all aspects of the analysis and reasoning
  processes.

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An Overview of VA at WPI
Transforms Abstractions
Data Sources
Discovery Reasoning
Interaction Spaces
Visual Representations
-Files -Databases -Numeric -Nominal
-Clustering -Sampling -Nominal to
ordinal -Dimension reduction
-Data (multiple) -Statistics -Structure
(hierarchy)
-Data -Structure (hierarchy)
-Clusters -Associations
-Nuggets -Outliers
-Spatial -Temporal -Quality
-Quality -Uncertainty -Missing values
-Data quality -Abstraction quality -Anomalies
-Events -Trends -Hypotheses
-Clutter reduction
-Streaming
-Evidence
-Past Work
-Recent Work
-Planned Work
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Examples of Projects
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Multiresolution Visualization

• For large datasets, visualizations quickly get
  cluttered
• We have extended all of our visualizations to
  work at multiple resolutions
• Hierarchical clustering generates many levels of
  detail
• User can select areas of interest to view at full
  resolution while the rest of the data is shown
  via cluster centers and extents (shown as bands
  of variable opacity)

This work was funded by NSF grant IIS-9732897
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Dimension Reduction

• Dimensions are hierarchically clustered based on
  similarity measures
• Hierarchy displayed using InterRing
• Users select clusters of dimensions or
  representative dimensions for detailed analysis

This work was funded by NSF grant IIS-0119276
42 dimension census dataset.
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Linking Spatial and Non-Spatial

• Diagonal plots of scatterplot matrix can have
  numerous uses
• Weve implemented histograms, line plots, and 2-D
  options
• Example show multispectral remote sensing data, 1
  layer per diagonal plot
• User can select in either 2-D or parameter space
  and see corresponding elements in other views.

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Layout Strategies

• Different layout strategies can reveal different
  patterns in the data
• Detecting, classifying, and measuring trends,
  outliers, repeated patterns, clusters, and
  correlations can be facilitated via specific
  layouts

Cyclic
Data Driven
Principal Components
Order Driven
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Visualizing Data with Nominal Fields

• Arbitrary assignment of non-numeric fields to
  numbers can lead to misinterpretation, lost
  patterns
• By looking at similarities in distributions
  across all dimensions, we can group values of a
  nominal variable with similar global
  characteristics
• Assignments used to convey order and relative
  distance

Original Assignment
Assignment after Correspondence Analysis
This work was funded by NSF grant IIS-0119276 and
funds from the NSA
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Visual Clutter Reduction

• In scenes with thousands of moving objects, there
  is need to reduce clutter
• Weve explored and developed many strategies,
  including
• Information-preserving
• Information-reducing
• Visual remapping

This work was funded by a grant from the AFRL
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Data Quality Visual Encoding

• Data quality refers to the degree of uncertainty
  of data
• Quality measures are visually encoded into
  existing visualizations
• This helps users focus on high quality data to
  draw reliable conclusions

This work was funded by NSF grant IIS-0414380
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Quality Space Visualization

• Quality space is visualized separately to convey
  patterns in the data quality measures
• Records or dimensions can be ordered by quality
  to reveal structure and relations
• Stripe view shows individual data value quality
  Histogram view shows summarization and
  distribution

StripeQualityMap
HistogramQualityMap
This work was funded by NSF grant IIS-0414380
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Interactions between Data Spaceand Quality Space

• Linking brush When users select a subset in one
  space, the corresponding subset in the other
  space will be highlighted accordingly.
• Sample figures The data points in the data space
  with high values in the third dimension are
  highlighted, then the distribution of quality
  measures for this subset is rendered in the
  quality map.

Data space with highlighting
LinkedQuality space
This work was funded by NSF grant IIS-0414380
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Nugget Management System (NMS)

• Nuggets are patterns, clusters, anomalies or
  other features of a data set that have been
  visually or computationally isolated.
• NMS helps users to extract, consolidate and
  manage nuggets during their visual exploration.
  NMS eventually builds a hypothesis view based on
  the nugget space to support or refute hypotheses
  of users.

Nugget Space
Hypothesis View
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Common Themes and Strategies

• Provide data and attributes in multiple, linked
  spaces
• Use automated and interactive tools for
  controlling and optimizing views
• Measure quality at all stages of the pipeline and
  convey to the user for decision support
• Assess quality measures by comparing them to user
  responses
• Manage scale via abstractions such as sampling
  and clustering, but communicate information loss
  to analyst to allow trade-offs
• Perform usability testing with all visualizations
  and interactive tools
• Release code to the public domain for widest
  possible impact

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Some References

• Hierarchical Parallel Coordinates
• Fua, Y.-H., Ward, M. O., and Rundensteiner, E.
  A., "Hierarchical Parallel Coordinates for
  Visualizing Large Multivariate Data Sets," IEEE
  Conf. on Visualization '99, Oct. 1999.
• Hierarchical Dimension Management
• Jing Yang, Matthew O. Ward, Elke A. Rundensteiner
  and Shiping Huang, "Visual Hierarchical Dimension
  Reduction for Exploration of High Dimensional
  Datasets", Proc. VisSym 2003.
• Jing Yang, Wei Peng, Matthew O. Ward and Elke A.
  Rundensteiner, "Interactive Hierarchical
  Dimension Ordering, Spacing and Filtering for
  Exploration of High Dimensional Datasets", IEEE
  Symposium on Information Visualization 2003
  (InfoVis 2003), pp 105 - 112, October 2003.
• Visual Clutter Measurement and Reduction
• Wei Peng, Matthew O. Ward and Elke A.
  Rundensteiner, "Clutter Reduction in
  Multi-Dimensional Data Visualization Using
  Dimension Reordering", IEEE Symposium on
  Information Visualization 2004 (InfoVis 2004), pp
  89 - 96, October 2004.
• Glyph Layout
• Matthew O. Ward, "A taxonomy of glyph placement
  strategies for multidimensional data
  visualization", Information Visualization, Vol 1,
  pp 194-210, 2002.
• Nominal Data Visualization
• Geraldine E. Rosario, Elke A. Rundensteiner,
  David C. Brown, Matthew O. Ward and Shiping
  Huang, "Mapping Nominal Values to Numbers for
  Effective Visualization", Information
  Visualization Journal, Vol 3, pp 80-95, 2004.
• Data Quality Visualization
• Z. Xie, S. Huang, M. Ward, and E. Rundensteiner,
  Exploratory Visualization of Multivariate Data
  with Variable Quality, Proc. IEEE Symposium on
  Visual Analytics Science and Technology, pp
  183-190, 2006.
• Zaixian Xie, Matthew O. Ward, Elke A.
  Rundensteiner, Shiping Huang, "Integrating Data
  and Quality Space Interactions in Exploratory
  Visualizations", The Fifth International
  Conference on Coordinated Multiple Views in
  Exploratory Visualization (CMV 2007), pp 47-60,
  July 2007.
• Discovery Management
• Di Yang, Elke A. Rundensteiner, Matthew O. Ward,
  "Nugget Discovery in Visual Exploration
  Environments by Query Consolidation", ACM CIKM
  2007, November, 2007
• Di Yang, Elke A. Rundensteiner, Matthew O. Ward,
  "Analysis Guided Visual Exploration to
  Multivariate Data", IEEE Symposium on Visual
  Analytics Science and Technology, October 2007.