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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.

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VisTrails: Overview


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    1. 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

    2. VisTrails: Managing Provenance • Provenance of computational artifacts is necessary to reproduce, validate and share scientific results

    3. 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

    4. 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 (Freire et al., IPAW 2006) Provenance can be as important as the results!

    5. Keeping Scientific Exploration Trails Workflows Trail • 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?

    6. User juliana eranders eranders eranders stevec Keeping Scientific Exploration Trails Trail Notes Initial visualization with z-scaling corrected Added texture and shading Added plane to visualize internal structure Found good transfer function Identified lesion tissue

    7. Provenance Beyond Reproducibility Scalable exploration of parameter spaces • Query workflows by example • Visual comparison of workflows and data products

    8. 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)

    9. 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)

    10. Integrating Tools and Libraries VisTrails add-on for ParaView SCIRun in VisTrails Workflow that combines 5 different libraries

    11. Applications and Users Psychiatry-U of Utah

    12. Applications and Users Physics-Cornell

    13. Applications and Users Environmental Science-OHSU

    14. 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.

    15. More info about VisTrails google vistrails Or http://www.vistrails.org