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dc.contributor.advisorSattar, Abdul
dc.contributor.authorGhanbari Ghooshchi, Nina
dc.date.accessioned2020-10-16T01:54:04Z
dc.date.available2020-10-16T01:54:04Z
dc.date.issued2020-09-30
dc.identifier.doi10.25904/1912/3980
dc.identifier.urihttp://hdl.handle.net/10072/398414
dc.description.abstractBusiness processes are essential parts of any organisation which define the series of steps performed to achieve their goal. Business processes are normally designed manually by business experts who have deep knowledge of the activities performed in the organi-sations. This knowledge is commonly described in a declarative way as business rules. Organisations have to cope with large numbers of business rules and existing regulations governing the business in which they operate. Such rules are difficult to maintain due to their size and complexity, and it is increasingly challenging to ensure that each business process adheres to those rules. As such, extraction of business processes from rules has three clear advantages: (1) visualisation of all possible executions allowed by the rules,(2) automated execution and compliance by design, (3) identification of “inefficiencies” in the business rules. Extraction of business processes from rules set is a time and re-source consuming process. In this thesis, we have investigated two approaches to extract the business processes from the declarative specification of it. In the first approach, we have investigated the application of constraint satisfaction based planners to automatically extract the business process from the sets of rules specifying it. For this purpose, we have developed a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). As a constraint-based planner, it encodes a planning problem as a constraint satisfaction problem and then extracts the plan from the solution to that. TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don’t cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planners. Experimental results we have conducted show that our developed planner outperforms existing constraint-based planners for a large set of benchmarks. Then we have described the specification of the activities and regulations in the organisation in PDDL, the language used for describing planning problems, and the business process is extracted from the resulted plan. In the second approach, to handle uncertainties which may occur during execution of the process and to have a comprehensive business process, we have also designed a formal method to visualise and operationalise sets of rules as a verifiable business process that is compliant by design, which allows us to analyse all possible execution paths. Additionally, we formally prove correctness of the business processes generated by our method. The approach is implemented in a tool and evaluated on both performance and correctness, showing that even for highly complex sets of rules the approach performs well and outperforms a well-known state-of-the-art approach. Evaluation on a real-life process shows the feasibility of the presented approach.
dc.languageEnglish
dc.language.isoen
dc.publisherGriffith University
dc.publisher.placeBrisbane
dc.subject.keywordsBusiness processes
dc.subject.keywordsbusiness rules
dc.subject.keywordsineffciencies
dc.subject.keywordsTransition Constraints for Parallel Planning (TCPP)
dc.subject.keywordsdomain transition graphs (DTG)
dc.titleConstraint-based Automated Planning and Business Process Modelling
dc.typeGriffith thesis
gro.facultyScience, Environment, Engineering and Technology
gro.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
gro.hasfulltextFull Text
dc.contributor.otheradvisorNewton, Muhammad A
gro.identifier.gurtID000000021704
gro.thesis.degreelevelThesis (PhD Doctorate)
gro.thesis.degreeprogramDoctor of Philosophy (PhD)
gro.departmentInst Integrated&IntelligentSys
gro.griffith.authorGhanbari Ghooshchi, Nina


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