ACM TechNews
Semantic Provenance for eScience: Managing the Deluge of Scientific Data
Internet Computing (08/08) Vol. 12, No. 4, P. 46; Sahoo, Satya S.; Sheth, Amit; Henson, Cory
Metadata that is essential to the effective management of exponentially
growing volumes of scientific data from industrial-scale experiment
protocols is known as provenance information in eScience. Expressive
provenance information and domain-specific provenance ontologies are
incorporated into the semantic provenance architecture for eScience data,
which applies the information to data management. The authors write that
provenance information has to be expressive and software-interpretable so
that it can be employed effectively for eScience data management, and to
accomplish this, the authors have combined the concept of provenance
information with domain knowledge and ontological underpinning. A new
approach that separates the task of producing high-quality semantic
provenance from the core functionality of workflow engines is called for,
and the authors present a "two-degrees-of-separation" strategy in which
semantic provenance creation is handled by specialized services that cite
one or more domain-specific provenance ontologies and can be embedded
within scientific workflows on demand, while a workflow engine would be
equipped with a set of services and a suite of domain-specific provenance
ontologies as resources that could be flexibly blended into a scientific
workflow based on user needs. The semantic provenance framework for
eScience is described by the authors as incorporating three basic
dimensions representing semantic provenance annotation, domain provenance
ontologies, and usage. The first dimension entails a set of specialized
tools interfacing with a scientific workflow on demand to generate
semantic-provenance information; the second dimension utilizes
domain-specific provenance ontologies to model scientific processes, data,
and agents as formally defined concepts connected via named relationships;
and the third dimension involves software agents using reasoning tools to
process the semantic-provenance information and answer sophisticated domain
queries.
full paper
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