Presenting Provenance Based on User Roles: Experiences with a Solar Physics Data Ingest System

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One goal of provenance is to provide users an understanding of the steps a system took to generate data products. Here, the level of detail captured by provenance becomes an important consideration. As detail is added, more questions can be hypothetically addressed. However, presenting significant provenance detail may also overwhelm end users, for one of two reasons: (i) the detail presented is irrelevant to the objectives, or (ii) the detail requires background knowledge a user lacks.

Both of these challenges are present for data generated by the Mauna Loa Solar Observatory's (MLSO) Advanced Coronal Observing System (ACOS). In ACOS, photometer-based readings are taken of solar activity and subsequently processed into data products consumable by end users. To fully understand these sequences of steps, background knowledge corresponding to various areas (e.g., astronomy, digital imaging, and ACOS specific techniques) is required by end users. This makes reviewing provenance difficult for users outside the ACOS development team, where varying degrees of background may be expected (ranging from outside domain experts in Solar Physics to citizen scientists). Likewise, even when steps taken by ACOS are understandable, they may provide undesired detail to an end user if presented.

The work with ACOS involved the development of a Semantic Web based framework to selectively present provenance detail for data products in ACOS. Here, provenance is captured according to two sets of ontologies, the Proof Markup Language, which is an ontology based domain-independent provenance model, and a step ontology, designed to capture hierarchies of provenance steps. Used in combination, these ontology sets enable the creation of multiple levels of provenance, ranging from coarse to fine grained detail. In this setting, users may choose to expand/collapse provenance steps to view desired details. However, the specific provenance details a user initially sees is defined through adoption of a given user role, defined through a role ontology, in which certain sets of background from the step ontology are assumed. In the context of ACOS, three user roles have been identified: ACOS expert, someone with complete background knowledge; outside domain expert, someone with knowledge in Solar Physics but not in ACOS-specific techniques; and citizen scientist, with only basic domain knowledge.

We present how we have enabled browsing of provenance through a semantically enabled framework, defined through the two ontology sets. And we conclude by discussing that while developed with ACOS-based provenance in mind, domain independence is preserved in the framework itself -- making it easily extensible to other eScience systems.


DateCreated ByLink
August 26, 2011
Patrick WestDownload

Related Projects:

SPCDIS Project LogoSemantic Provenance Capture in Data Ingest Systems (SPCDIS)
Principal Investigator: Peter Fox
Co Investigator: Deborah L. McGuinness
Description: The goal of this project is to develop at the RPI Tetherless World Constellation, based within the NCAR High Altitude Observatory and in collaboration with the University of Texas at El Paso, the University of Michigan and McGuinness Associates a semantically-enabled data ingest capability.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts: eScience
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: , Semantic Web
Semantic eScience
Lead Professor: Peter Fox
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: eScience