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 ( – Rensselaer Polytechnic Institute ( AGUFM2010-IN43C-05 1

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 2

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 3

Complexity 4

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 5

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 6

Multiple-domain knowledgebase Objective: Use Semantic Web technologies to combine provenance from different sources in an interoperable fashion. 7 Provenance Ontology Solar Physics Domain Data Processing Domain Extension of work on Virtual Solar Terrestrial Observatory Good/Bad/Ugly (GBU) ratings, Trust, Quality flags Proof Markup Language (PML)

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

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

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

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 11

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

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 13

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. 14

References L. Moreau, “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), 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 ,