1

Capturing and Using Knowledge about Visualization Toolkits

Nicholas Del RioPaulo Pinheiro - PNNL

2

Outline

• Discovery through Visualization Diversity• Background on Generating Visualizations• Visualization Conceptual Model• Visualization Knowledge Base• A Practical Application• Conclusion

3

Service Discovery for Visualization

Near neighbor vs. surface gridding techniques3D views:

isosurfaces vs. point plot

In many cases, it is up to the users to understand the different views and know how to generate them

There are many ways to visualize a single dataset

4

Service Discovery for Visualization

Near neighbor vs. surface gridding techniques3D views:

isosurfaces vs. point plot

How can we support the seamless automated discovery of visualization services to support visualization diversity?

There are many ways to visualize a single dataset

5

Goal

Objectives:

Enable automated discovery and integration of visualization services

1 Abstract visualization pipelines in the form of declarative requests (visualization queries)

2 Construct a knowledge base of visualization services

3 Develop methods for translating the abstractions into pipelines (query answering)

VISUALIZE http://cs.utep.edu/dataX.xyzAS isosurfaces IN firefox

WHERE FORMAT = csvAND TYPE = gravityAND interval = 5AND xRotation = 10

6

Proposed Usage Pattern

Users may also generate other visualizations of the same dataset from a variety of sources

7

Background

Sequence of visualization operators known as a pipeline

Users typically employ visualization toolkits to construct visualizations

8

Visualization Pipeline Structure

Op 1

Op 2

Op 3

Op 4

Op 5

Op1: vtkDataObjectToDataSetFilter

Op2: vtkShepardMethod

Op 3: vtkExtractVOI

Op 4: vtkContourFilter

Op 5: vtkPolyDataMapper

Mapping

Visualization Abstraction

Data Gathering

Rendering

2

Data Flow Model – Haber and McNabb 90 Data State Model – Chi 98

1

3

4

1 2 4 2 3

specified in the query

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

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• Functionally dependent These tools were not designed to operate on

general kinds of data and are were more focused towards information visualization

11

Our Enhancements: Format [Type]

Op 1

Op 2

Op 3

Op 4

Op 5

One way we expand on existing visualization models is by considering type and format requirements of modules. We call these modules transformers

CSV [Gravity]

XML [vtkPolyData]

XML [vtkImageData3D]

XML [vtkImageData2D]

Dimension reduction is not explicitly specified but inferred through the type requirements

Format is not enough, some can encode a variety of types

These formats and types should be defined in ontologies and shared to foster interoperability

OBSERVATION 1

OBSERVATION 2

OBSERVATION 3

XML [vtkPolyData]

JPEG [owl:Thing]

12

Our Enhancements: Viewer

Op 1

Op 2

Op 3

Op 4

Op 5

We also consider the viewer that presents the visualization

After the mapping, there may be a number of transformations before the geometry can be presented by a viewer

PDF [owl:Thing]

Op 6

JPEG [owl:Thing]

These additional transformations may be viewed as an expansion of the rendering phase

PDF Viewer(Type Agnostic)

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 Viewer4. Expands rendering phase to consist of a sub pipeline

of further transformations5. Is encoded in OWL

14

Constructing the Knowledge BaseWe 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

MapperTransformer Viewer

15

A Data Centric View

16

Answering Visualization QueriesVisualization Queries Specify:• Source format[type]• Target Visualization Abstraction• Target Viewer

MapperTransformer Viewer

VISUALIZE http://somedata.csvAS 3d-point-plot IN firefox WHERE FORMAT = csv AND TYPE = gravity-data

17

Sharing Visualizations

1. Send image (contents or by URL)

2. Send data

Recipient may be unable to adjust any properties such as contour interval, color tables, projection

and labels

Recipient may not have tools, capabilities, and expertise to regenerate visualization from data

3. Send URL of visualization embedded in viewer

These solutions have been implemented only for specific domains , for example OGC

VisKo queries address the limitations above

4. Send a VisKo Query specifying the visualization

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.

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

20

Play With Our System!

http://trust.utep.edu/visko http://iw.cs.utep.edu/visko-web: VisKo Server