---
title: "An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts"
authors:
  - "Rui Zhao"
doi: "10.24963/ijcai.2021/696"
url: "https://ijcai.org/proceedings/2021/696"
pdf: "https://ijcai.org/proceedings/2021/0696.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4929-4930"
track: "Doctoral Consortium"
keywords:
  - "Agent-based and Multi-agent Systems: Normative systems"
  - "Multidisciplinary Topics and Applications: Security and Privacy"
  - "Planning and Scheduling: Model-Based Reasoning"
---

# An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts

Rui Zhao

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4929-4930. DOI: [10.24963/ijcai.2021/696](https://doi.org/10.24963/ijcai.2021/696)

## Abstract

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.