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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

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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

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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

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Complexity

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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

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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

Multiple-domain knowledgebase

• Objective: Use Semantic Web technologies to combine provenance from different sources in an interoperable fashion.

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Provenance Ontology

Solar Physics Domain

Data

Processing

Domain

Extension of work on Virtual Solar

Terrestrial Observatory

http://www.vsto.org

Good/Bad/Ugly (GBU) ratings,

Trust, Quality flags

Proof MarkupLanguage (PML)http://inference-web.org

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Advanced CoronalObserving System

Mauna Loa Solar Observatory (MLSO)Hawaii

Intensity Images (GIF)

• Raw Image Data

Raw Image DataCaptured by CHIPChromosphericHelium-I ImagePhotometer

• 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

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Provenance View –Citizen Scientist

Data Capture (MLSO)

Data Processing (NCAR)

Quality Check (NCAR) Good/Bad/

UglyRating

• Raw Image Data

• Calibrated Image Data

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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

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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

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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 schemaOWL – Web Ontology Language

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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

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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.

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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.