Regulations are introduced by governments to ensure the well-being, safety, and other societal needs of citizens and enterprises. Governments also create programs aiming to improve awareness about and compliance with regulations. Goal models have been used in the past to conceptualize regulations and to measure compliance assessments. However, regulators often have difficulties assessing the performance of their regulations and programs. In this paper, we model both regulations and regulatory programs with the Goal-oriented Requirement Language. Using the same conceptualization framework enables asking questions about performance and about the evidence-based impact of programs on regulations. We also investigate how Watson Analytics, a cloud-based data exploration service from IBM, can be used pragmatically to explore and visualize goal satisfaction data to understand compliance issues and program effectiveness. A simplified example inspired from a Canadian mining regulation is used to illustrate the many opportunities of Watson Analytics in that context, and some of its current limitations.


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Title Goal-Oriented Regulatory Intelligence: How Can Watson Analytics Help?
Authors O. Akhigbe, S. Heap, S. Islam, D. Amyot and J. Mylopoulos
Type Conference
Conference/Journal Title International Conference on Concept Modeling
Volume/Number Vol-10650
Editors H. C. Mayr, G. Guizzardi, H. Ma and O. Pastor
Publisher Springer
Month October
Year 2017
Pages 77-91
DOI 10.1007/978-3-319-69904-2_7
Keywords Data analytics, Data visualization, Goal models, Goal-oriented Requirement Language, GoRIM, Regulatory compliance, Regulatory intelligence, Watson, Analytics
Topic revision: r1 - 25 May 2018, DanielAmyot
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