SeGoFlow: A Semantic Governance Workflow Tool
Data management increasingly demands transparency with respect to data processing. Various stakeholders need information tailored to their needs, e.g. data management plans (DMP) for funding agencies or privacy policies for the public. DMPs and privacy policies are just two examples of documents describing aspects of data processing. Dedicated tools to create both already exist. However, creating each of them manually or semi-automatically remains a repetitive and cognitively challenging task. We propose a data-driven approach that semantically represents the data processing itself as workflows and serves as a base for different kinds of result-sets, generated with SPARQL, i.e. DMPs. Our approach is threefold: (i) users with domain knowledge semantically represent workflow components; (ii) other users can reuse these components to describe their data processing via semantically enhanced workflows; and, based on the semantic workflows, (iii) result-sets are automatically generated on-demand with SPARQL queries. This paper demonstrates our tool that implements the proposed approach, based on a use-case of a researcher who needs to provide a DMP to a funding agency to approve a proposed research project.
KeywordsProvenance Workflow Governance Data management
- 2.Gil, Y., Cheung, W.K., Ratnakar, V., Chan, K.k.: Privacy enforcement in data analysis workflows. In: Proceedings of the 2007 PEAS Workshop, pp. 41–48 (2007)Google Scholar
- 3.Gil, Y., Fritz, C.: Reasoning about the appropriate use of private data through computational workflows. In: AAAI Spring Symposium: Intelligent Information Privacy Management (2010)Google Scholar