On the relevance of a business constraint to an event log

Ciccio, Claudio Di; Maggi, Fabrizio Maria; Montali, Marco; Mendling, Jan

doi:10.1016/j.is.2018.01.011

Cited by 20 publications

(17 citation statements)

References 68 publications

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“…(3) Candidate constraints are filtered, retaining only those whose support exceeds a given threshold. (4) Further filters (e.g., considering the "relevance" of a constraint [6]) are applied. (5) The overall model is checked for satisfiability, operating with different strategies if it is not; this is necessary since constraints with high support, but less than 1, may actually conflict with each other [4,5].…”

Section: Discovering Probdeclare Models From Event Logsmentioning

confidence: 99%

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Extending Temporal Business Constraints with Uncertainty

Maggi

Montali

Peñaloza

et al. 2020

Lecture Notes in Computer Science

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Monitoring and conformance checking are fundamental tasks to detect deviations between the actual and expected courses of execution in a business process. In a variety of application domains, the model capturing the expected behaviors may be intrinsically uncertain, e.g., to distinguish between standard courses of execution and exceptional but still conforming ones. Surprisingly, only very few approaches consider uncertainty as a first-class citizen in this spectrum. In this paper, we tackle this timely and challenging problem in the setting where the process of interest is described as a set of declarative, temporal business constraints. Our contribution is threefold. First, we delve into the conceptual meaning of probabilistic constraints and their semantics. Second, we argue that probabilistic constraints can be discovered from event data using existing techniques for declarative process discovery. Third, we study how to monitor probabilistic constraints, where constraints and their combinations may be in multiple monitoring states at the same time, though with different probabilities.

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Section: Discovering Probdeclare Models From Event Logsmentioning

confidence: 99%

“…This probabilistic monitoring technique has been fully implemented. 6 For solving systems of inequalities, we use the LP solver 7 . The implementation comes with various optimizations.…”

Section: Full Monitoringmentioning

confidence: 99%

Extending Temporal Business Constraints with Uncertainty

Maggi

Montali

Peñaloza

et al. 2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Candidate constraints are filtered by retaining only those whose support exceeds a given threshold. (4) Further filters (e.g., considering the "relevance" of a constraint [4]) are applied. (5) The overall model is checked for consistency, operating with different strategies if it is not.…”

Section: Discovering Probdeclare Models From Event Logsmentioning

confidence: 99%

“…To tackle these issues, the probabilistic temporal logic over finite traces PLTL f , and its fragment PLTL 0 f , have been recently proposed in [12]. 4 PLTL 0 f is of particular interest here, since the way probabilities are used can be interpreted by naturally matching the notion of conformance: a constraint ϕ has probability p, if, by imagining all the traces contained in a log, the fraction p of such traces satisfies ϕ.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Conformance Checking Based on Declarative Process Models

Maggi

Montali

Peñaloza

2020

Advanced Information Systems Engineering

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Monitoring and conformance checking are fundamental tasks to detect deviations between the actual and the expected courses of execution in a business process. In a variety of application domains, the model capturing the expected behaviors may be intrinsically uncertain, e.g., to distinguish between standard courses of execution and exceptional but still conforming ones. Surprisingly, only very few approaches consider uncertainty as a first-class citizen in this spectrum. In this paper, we tackle this timely and challenging problem considering the setting where the model is described as a set of declarative, temporal business constraints. First, we delve into the conceptual meaning of probabilistic business constraints, and argue that they can be discovered from event data using already existing techniques. Second, we study how to monitor probabilistic constraints, where constraints and their combinations may be in multiple monitoring states at the same time, though with different associated probabilities. Third, we operationalize this monitoring framework using automata coupled with probabilities, and report on its actual implementation.

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“…In this first step, we generate SHACL compliance constraints for a target process. As input, we take a set of DECLARE constraints, which can be automatically discovered with tools such as MINERful [12]. Table 2 shows an excerpt of the declarative constraints retrieved by MINERful from the real-life Sepsis event log [35].…”

Section: Declare To Shaclmentioning

confidence: 99%

Finding Non-compliances with Declarative Process Constraints Through Semantic Technologies

Ciccio

Ekaputra

Cecconi

et al. 2019

Lecture Notes in Business Information Processing

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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 percase checks, a variety of constraint languages and approaches have been developed in recent years to provide automated compliance checking. A salient example is DE-CLARE, 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 realworld event logs. Finally, we discuss its implications and future research directions.

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On the relevance of a business constraint to an event log

Cited by 20 publications

References 68 publications

Extending Temporal Business Constraints with Uncertainty

Extending Temporal Business Constraints with Uncertainty

Probabilistic Conformance Checking Based on Declarative Process Models

Finding Non-compliances with Declarative Process Constraints Through Semantic Technologies

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