VisTrails: Overview

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VisTrails: Overview Juliana Freire University of Utah http://www.vistrails.org nt work with: k Andersen, Steven P. Callahan , David Koop, Emanuele Santos , los E. Scheidegger , Claudio Silva and Huy T. Vo

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VisTrails: Overview. Juliana Freire University of Utah http://www.vistrails.org. Joint work with: Erik Andersen, Steven P. Callahan, David Koop, Emanuele Santos, Carlos E. Scheidegger, Claudio Silva and Huy T. Vo. VisTrails: Managing Provenance. - PowerPoint PPT Presentation

Transcript of VisTrails: Overview

Page 1: VisTrails: Overview

VisTrails: Overview

Juliana Freire University of Utah

http://www.vistrails.org

Joint work with:Erik Andersen, Steven P. Callahan, David Koop, Emanuele Santos, Carlos E. Scheidegger, Claudio Silva and Huy T. Vo

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VisTrails: Managing Provenance

Provenance of computational artifacts is necessary to reproduce, validate and share scientific results

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VisTrails: Managing Provenance

Provenance of computational artifacts is necessary to reproduce, validate and share scientific results

VisTrails provides comprehensive provenance infrastructure for computational tasks– Provenance is captured transparently– Efficient storage and intuitive query interfaces for exploring

provenance data– Support for collaboration

Designed to support exploratory tasks such as visualization and data mining– Task specification iteratively refined as users generate and

test hypotheses VisTrails is open source: www.vistrails.org

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Provenance for Workflows Treat a workflow (computational task)as a first-

class data product In exploratory tasks, series of workflows are

created and refined: change is the norm Keep exploration trail

Provenance can be as important as the results!

(Freire et al., IPAW 2006)

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Keeping Scientific Exploration Trails

TrailWorkflows

Uniformly captures data and workflow provenance– Data provenance: where does a specific data product

come from?– Workflow evolution: how has workflow structure changed

over time?

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Keeping Scientific Exploration Trails

Trail

Initial visualization

with z-scaling

corrected

Notes

Added texture and

shading

Added plane to visualize internal structure

Found good transfer function

Identified lesion tissue

User

juliana

eranders

eranders

eranders

stevec

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Provenance Beyond Reproducibility

Scalable exploration of parameter spaces

Visual comparison of workflows and data products

Query workflows by example

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Querying Workflows

Workflows are graphs: hard to specify queries using text

Querying workflows by example– WYSIWYQ -- What You See Is What You Query– Interface to create workflow is same as to query

(Scheidegger et al., TVCG 2007)

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Creating Workflows by Analogy

Simplify creation of workflows Use the wisdom of the crowds

– Some workflow refinements are common, e.g., change the rendering technique, publish image on the Web

Apply refinements by analogy, automatically

Source

Target

(Scheidegger et al., TVCG 2007)

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Integrating Tools and Libraries

SCIRun in VisTrails

Workflow that combines 5 different libraries

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

VisTrails add-on for ParaView

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Applications and Users

Psychiatry-U of Utah

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Applications and Users

Physics-Cornell

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Applications and Users

Environmental Science-OHSU

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Acknowledgments

This work is partially supported by the National Science Foundation, the Department of Energy, an IBM Faculty Award, and a University of Utah Seed Grant.

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More info about VisTrails

google vistrails

Or

http://www.vistrails.org