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Semantic Provenance for Solar Physics Data Ingest System Roles

This presentation outlines user role-based experiences with a solar physics data ingest system, focusing on applying provenance to manage complexity for different kinds of users, including citizen scientists and domain experts. It discusses the challenges and benefits of abstracting fine-grained provenance and using Semantic Web technologies to combine data sources.

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Semantic Provenance for Solar Physics Data Ingest System 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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