An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts
This addresses data governance compliance challenges for organizations in decentralized, data-intensive environments, though it appears incremental as it builds on existing formal methods.
The paper tackles the problem of automated compliance checking for data governance rules in decentralized MIMO contexts by proposing Dr.Aid, a logic-based AI framework that models rules using situation calculus and checks compliance over data-flow graphs, with evaluation on real-world datasets from data-intensive research.
We propose Dr.Aid, a logic-based AI framework for automated compliance checking of data governance rules over data-flow graphs. The rules are modelled using a formal language based on situation calculus and are suitable for decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid models data rules and flow rules and checks compliance by reasoning about the propagation, combination, modification and application of data rules over the data flow graphs. Our approach is driven and evaluated by real-world datasets using provenance graphs from data-intensive research.