Presented at AAAI Fall Symposium on Discovery Infomatics

1. Capturing and Using Knowledge
about Visualization Toolkits
Nicholas Del Rio
Paulo Pinheiro - PNNL
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2. Outline
• Discovery through Visualization Diversity
• Background on Generating Visualizations
• Visualization Conceptual Model
• Visualization Knowledge Base
• A Practical Application
• Conclusion
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3. Service Discovery for Visualization
There are many ways to
visualize a single dataset
Near neighbor vs. surface
3D views: gridding techniques
isosurfaces
vs. point plot
In many cases, it is up to the users to understand the different
views and know how to generate them 3

4. Service Discovery for Visualization
There are many ways to
visualize a single dataset
Near neighbor vs. surface
3D views: gridding techniques
isosurfaces
vs. point plot
How can we support the seamless automated discovery of
visualization services to support visualization diversity? 4

5. Goal
Enable automated discovery and integration of
visualization services VISUALIZE http://cs.utep.edu/dataX.xyz
AS isosurfaces IN firefox
Objectives: WHERE
AND
FORMAT
TYPE
= csv
= gravity
AND interval =5
AND xRotation = 10
Abstract visualization pipelines in
1 the form of declarative requests
(visualization queries)
Construct a knowledge base of
2 visualization services
Develop methods for translating
3 the abstractions into pipelines
(query answering) 5

6. Proposed Usage Pattern
Users may also generate other visualizations of the same dataset from a variety of sources
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7. Background
Users typically employ visualization
toolkits to construct visualizations
Sequence of visualization
operators known as a
pipeline
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8. Visualization Pipeline Structure
Op1: vtkDataObjectToDataSetFilter Op
Data Gathering 1
1
Op
Op2: vtkShepardMethod 2
Mapping 2
Op
Op 3: vtkExtractVOI 3
Op 4: vtkContourFilter Op Visualization Abstraction 3
4
specified in the query
Op
Op 5: vtkPolyDataMapper 5 Rendering 4
Data Flow Model – Haber and McNabb 90 1 2 4 Data State Model – Chi 98 2 3 8

9. Building From Existing Work
Haber’s work paved the way for modular visualization
environments popular in the 90’s:
– Visualization Data Explorer (OpenDX) and IRIS
– A Visualization System (AVS) and Visualization Toolkit (VTK)
– Users still have to manually compose pipelines
Chi’s work provided a data centric perspective from
which to compare and taxonomize techniques
These models have not been used to drive automatic
composition of visualization pipelines
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10. Building From Existing Work (2)
Past efforts to automate visualization generation have
had great success in restricted domains:
• A Presentation Tool (APT) – Jock Mackinlay 86
• Tableau – Stolte 2012
Both operate on relational data to drive visualizations:
• Nominal or ordinal
These tools were not designed to operate on
• Functionally dependent
general kinds of data and are were more
focused towards information visualization
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11. Our Enhancements: Format [Type]
One way we expand on existing visualization models is by considering type and
format requirements of modules. We call these modules transformers
CSV [Gravity]
OBSERVATION 1
Op
1
Format is not enough, some can
XML [vtkPolyData] encode a variety of types
Op
2
OBSERVATION 2
XML [vtkImageData3D]
Dimension reduction is not explicitly
Op
3
specified but inferred through the
type requirements
XML [vtkImageData2D]
Op
4 OBSERVATION 3
XML [vtkPolyData]
These formats and types should be
defined in ontologies and shared to
Op
5
foster interoperability
JPEG [owl:Thing] 11

12. Our Enhancements: Viewer
We also consider the viewer that presents the visualization
Op
1
Op After the mapping, there may be a number of transformations
2
before the geometry can be presented by a viewer
Op
3 These additional transformations may
JPEG [owl:Thing] PDF [owl:Thing] be viewed as an expansion of the
Op
rendering phase
4
Op Op
5 6
PDF Viewer
(Type Agnostic)
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13. Our Overall Model
1. Marries Data Flow (Haber) with Data State (Chi)
concept of Visualization Abstraction
2. Incorporates service composition concerns
(i.e., format [type]) into data gathering phase
3. Incorporates concept of a Viewer
4. Expands rendering phase to consist of a sub
pipeline of further transformations
5. Is encoded in OWL
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14. Constructing the Knowledge Base
We can classify visualization services using the concepts in our ontology:
• Transformer
• Mapper (generates visualization abstractions)
• Viewer
Services are combined based on our model constraints:
• Format[type] match-ups
• Must include a mapper
• Must terminate at a viewer
Transformer Mapper Viewer
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15. A Data Centric View
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16. Answering Visualization Queries
VISUALIZE http://somedata.csv Visualization Queries Specify:
AS 3d-point-plot IN firefox • Source format[type]
WHERE FORMAT = csv AND • Target Visualization Abstraction
TYPE = gravity-data • Target Viewer
Transformer Mapper Viewer
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17. Sharing Visualizations
Recipient may be unable to adjust
any properties such as contour
interval, color tables, projection
and labels
1. Send image (contents or by URL)
Recipient may not have
tools, capabilities, and
expertise to regenerate
visualization from data
2. Send data
These solutions have been
implemented only for specific
domains , for example OGC
3. Send URL of visualization embedded in viewer
VisKo queries address
the limitations above
4. Send a VisKo Query specifying the visualization 17

18. Conclusion
• Visualization queries abstract away the complexities
of visualization pipelines.
• We can automate pipeline construction provided:
– A visualization query
– A service knowledge base structured using our model
• We can use queries to share visualizations in a way
that empowers visualization recipients.
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19. Future Work
• Automated Parameter Settings
– Color functions driven from formula identification
– Data driven vs. visualization driven
• Weighted graphs
– Add information about performance
– Add information about quality degradation
• Task driven generation
– Map task descriptions (Shneiderman 96) to the right set of
parameters and visualization abstractions
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20. Play With Our System!
http://trust.utep.edu/visko
http://iw.cs.utep.edu/visko-web: VisKo Server
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