Business process compliance checking enables organisations to assess whether their processes fulfil a given set of constraints, such as regulations, laws, or guidelines. Whilst many process analysts still rely on ad-hoc, often handcrafted per-case checks, a variety of constraint languages and approaches have been developed in recent years to provide automated compliance checking. A salient example is Declare, a well-established declarative process specification language based on temporal logics. Declare specifies the behaviour of processes through temporal rules that constrain the execution of tasks. So far, however, automated compliance checking approaches typically report compliance only at the aggregate level, using binary evaluations of constraints on execution traces. Consequently, their results lack granular information on violations and their context, which hampers auditability of process data for analytic and forensic purposes. To address this challenge, we propose a novel approach that leverages semantic technologies for compliance checking. Our approach proceeds in two stages. First, we translate Declare templates into statements in SHACL, a graph-based constraint language. Then, we evaluate the resulting constraints on the graph-based, semantic representation of process execution logs. We demonstrate the feasibility of our approach by testing its implementation on real-world event logs. Finally, we discuss its implications and future research directions.

Finding non-compliances with declarative process constraints through semantic technologies / Di Ciccio, C.; Ekaputra, F. J.; Cecconi, A.; Ekelhart, A.; Kiesling, E.. - 350:(2019), pp. 60-74. (Intervento presentato al convegno 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019 tenutosi a Rome; Italy) [10.1007/978-3-030-21297-1_6].

Finding non-compliances with declarative process constraints through semantic technologies

Di Ciccio C.
;
2019

Abstract

Business process compliance checking enables organisations to assess whether their processes fulfil a given set of constraints, such as regulations, laws, or guidelines. Whilst many process analysts still rely on ad-hoc, often handcrafted per-case checks, a variety of constraint languages and approaches have been developed in recent years to provide automated compliance checking. A salient example is Declare, a well-established declarative process specification language based on temporal logics. Declare specifies the behaviour of processes through temporal rules that constrain the execution of tasks. So far, however, automated compliance checking approaches typically report compliance only at the aggregate level, using binary evaluations of constraints on execution traces. Consequently, their results lack granular information on violations and their context, which hampers auditability of process data for analytic and forensic purposes. To address this challenge, we propose a novel approach that leverages semantic technologies for compliance checking. Our approach proceeds in two stages. First, we translate Declare templates into statements in SHACL, a graph-based constraint language. Then, we evaluate the resulting constraints on the graph-based, semantic representation of process execution logs. We demonstrate the feasibility of our approach by testing its implementation on real-world event logs. Finally, we discuss its implications and future research directions.
2019
31st International Conference on Advanced Information Systems Engineering, CAiSE 2019
compliance checking; process mining; RDF; SHACL; SPARQL
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Finding non-compliances with declarative process constraints through semantic technologies / Di Ciccio, C.; Ekaputra, F. J.; Cecconi, A.; Ekelhart, A.; Kiesling, E.. - 350:(2019), pp. 60-74. (Intervento presentato al convegno 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019 tenutosi a Rome; Italy) [10.1007/978-3-030-21297-1_6].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1362048
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