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Presenting Provenance Based on User Roles

Presenting Provenance Based on User Roles. Experiences with a Solar Physics Data Ingest System. Patrick West, James Michaelis, Peter Fox, Stephan Zednik, Deborah McGuinness – Tetherless World Constellation (http://tw.rpi.edu) – Rensselaer Polytechnic Institute (http://www.rpi.edu).

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Presenting Provenance Based on User Roles

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  1. Presenting ProvenanceBased on User Roles Experiences with a Solar Physics Data Ingest System Patrick West, James Michaelis, Peter Fox, Stephan Zednik, Deborah McGuinness – Tetherless World Constellation (http://tw.rpi.edu) – Rensselaer Polytechnic Institute (http://www.rpi.edu) AGUFM2010-IN43C-05

  2. Outline of Presentation • Prior Work in Selective Provenance Presentation • Rationale for User Roles in Presentation • Our Focus Area: • Semantic Provenance Capture in Data Ingest Systems (SPCDIS) • Advanced Coronal Observing System (ACOS) • Applying user roles to provenance

  3. Prior Work • Significant prior work on provenance views + abstractions (Moreau, 2009) • Two kinds approaches: • Expanding Abstract Provenance (Hunter, 2007) • Start with abstract provenance, expand to fine grained • Abstracting Fine Grained Provenance (Davidson, 2008) • Start with fine-grained, select desired components, then abstract away unwanted detail • Common goal: manage complexity of provenance

  4. Complexity

  5. Kinds of Users • In context of a Solar Physics Data System, two kinds of expertise: • Scientific (Astro/Solar Physics) • Technical (Pipeline + components) • Kinds of Users: • Project coordinators • Knowledgeable in both science and technical • Outside Domain Experts • Citizen Scientists

  6. Rationale for User Roles • Different backgrounds for different users • E.g., Domain Experts versus Citizen Scientists • Abstract -> Fine-grained: can be time intensive process • Fine-grained -> Abstract: requires background to know what you’re looking for • Key idea: Initial presentation of provenance components can be important for end-users • Finer grained components for experts • Abstract components for novices

  7. Multiple-domain knowledgebase • Objective: Use Semantic Web technologies to combine provenance from different sources in an interoperable fashion. • Proof Markup • Language (PML) • http://inference-web.org Extension of work on Virtual Solar Terrestrial Observatory http://www.vsto.org Good/Bad/Ugly (GBU) ratings, Trust, Quality flags

  8. Advanced Coronal Observing System Intensity Images (GIF) Velocity Images (GIF) Raw Image Data Captured by CHIP Chromospheric Helium-I Image Photometer Publishes Mauna Loa Solar Observatory (MLSO) Hawaii National Center for Atmospheric Research (NCAR) Data Center. Boulder, CO • Raw Image Data • Raw Data Capture • Follow-up Processing • on Raw Data • Quality Checking • (Images Graded: GOOD, BAD, UGLY)

  9. Provenance View –Citizen Scientist Data Capture (MLSO) • Raw Image Data Data Processing (NCAR) • Calibrated Image Data Quality Check (NCAR) Good/Bad/Ugly Rating

  10. Provenance View –Domain Expert Data Processing Quality Check Data Capture (MLSO) Flat Field Calibration Hot Pixel Correction Centering/Trimming/Clipping Compute Sample Means Determine Test Channel Assign GBU Rating Good/Bad/Ugly Rating

  11. Use Cases • Different users wish to get overview of provenance for quality rating. • Citizen Scientist: • Sees high-level provenance. • Wishes to know more about how Good/Bad/Ugly rating created • Expands Quality Check node. • Domain Expert: • Starts with fine-grained provenance view, generates abstraction exposing quality check processes: • Compute Sample Means • Determine Test Channel • Assign Good/Bad/Ugly Rating

  12. Applying user roles • Semantic Web (RDFS/OWL) Ontologies for defining domain knowledge needed. Specifically for defining: • Workflow components. • User roles. • Component-Role Mapping. RDFs – Resource Description Framework schema OWL – Web Ontology Language

  13. Ongoing Issues • Some inherent challenges • Deciding on how to map components to roles. • Will a given user necessarily fit into one of the pre-defined roles? • Key research question pursued • For preserving provenance interface usability, what a good middle ground between: • Going from abstract to fine-grained provenance • As well as fine-grained to abstract provenance

  14. Summary • Managing complexity is an important activity for presenting provenance. • Just providing drill-down from abstract to more detailed views or fine-grained selection is not enough. • The user can be provided an initial presentation of content based on their level of knowledge, from general interest to domain expert. • What is needed is an approach that provides the right level of initial explanation based on the user’s role.

  15. References • L. Moreau, 2009. “The foundations for provenance on the web.” • K. Cheung, J. Hunter, and Lashtabeg, A. and J. Drennan “SCOPE: a scientific compound object publishing and editing system.” International Journal of Digital Curation, 3(2), 2008. • S. Cohen-Boulakia, O. Biton, S. Cohen and S. Davidson “Addressing the provenance challenge using ZOOM.” Concurrency and Computation: Practice and Experience, 20(5), p. 497-506, 2008.

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