Automated business rule generator from business process model based on fragment of First-Order Logic

Student thesis: Master's Thesis


Hamda Al-Ali. “Automated business rule generator from business process model based on fragment of First-Order Logic.' MSc. Thesis by Research in Engineering, Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, United Arab Emirates, April 2018. Business process contains a massive amount of data that must be correct, complete and up to date. These data can be shared and manipulated across multiple departments within an organization; therefore there is an essential need to ensure that business process complies with the organization goals and strategies. Process model are used to reflect the business process, maintain consistency between different process instances and help stockholders to communicate and share ideas clearly. For many cases, rules and regulations can be hardcoded in theses models. Business rules are widely used to ensure the alignment between the business process and regulations, measure the conformance between the business process and the process model and discover gaps in the process execution. Many approaches have been proposed to formulate business rules in different syntaxes and employ them to define or constrain certain aspects of the process. However, most of them use regulations and process descriptions as a baseline to write these rules. In this thesis, we introduce a simple, human-readable rule language based on a fragment of First-Order Logic (FOL) and show how compliance rules can be generated directly from BPMN models. We focus on control flow aspects of BPMN models by 1) transforming the model to obtain a uniform representation of task activation 2) dividing the model into sets of components and 3) using our proposed language to generate compliance rules for each component. We started by surveying the current approaches available in the literature then we developed the tool to automatically extract business rules from the process model and finally we evaluated our approach which showed that 1) rule generation time increases with the number of objects in the process model 2) applicability of our approach in conformance checking 3) our FOL rules can be translated to any business tool format 4) the correctness of our approach using logic programming.
Date of AwardApr 2018
Original languageAmerican English
SupervisorErnesto Damiani (Supervisor)


  • BPMN
  • BPM
  • FOL
  • Process mining
  • Conformance checking.

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