Application of named graphs towards custom provenance views
TAPP'09 First workshop on on Theory and practice of provenance
Querying and Managing Provenance through User Views in Scientific Workflows
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
The Foundations for Provenance on the Web
Foundations and Trends in Web Science
A semantic portal for next generation monitoring systems
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
TWC International Open Government Dataset Catalog
Proceedings of the 7th International Conference on Semantic Systems
A new approach for publishing workflows: abstractions, standards, and linked data
Proceedings of the 6th workshop on Workflows in support of large-scale science
A case study for integrating public safety data using semantic technologies
Information Polity - Special issue on Public Engagement and Government Collaboration: Theories, Strategies and Case Studies
Open data kit: tools to build information services for developing regions
Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development
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As Open Data becomes commonplace, methods are needed to integrate disparate data from a variety of sources. Although Linked Data design has promise for integrating world wide data, integrators often struggle to provide appropriate transparency for their sources and transformations. Without this transparency, cautious consumers are unlikely to find enough information to allow them to trust third party content. While capturing provenance in RPI's Linking Open Government Data project, we were faced with the common problem that only a portion of provenance that is captured is effectively used. Using our water quality portal's use case as an example, we argue that one key to enabling provenance use is a better treatment of provenance granularity. To address this challenge, we have designed an approach that supports deriving abstracted provenance from granular provenance in an open environment. We describe the approach, show how it addresses the naturally occurring unmet provenance needs in a family of applications, and describe how the approach addresses similar problems in open provenance and open data environments.