Stochastic analysis of BPMN with time in rewriting logic

Durán, Francisco; Rocha, Camilo; Salaün, Gwen

doi:10.1016/j.scico.2018.08.007

Cited by 28 publications

(18 citation statements)

References 44 publications

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“…Note also that rules update the information on execution times, task durations, etc. (see, e.g., the update of the task-tstamps attribute, lines [13][14][15][16]. This information is important for analysis purposes, as it will be seen in Section 4.…”

Section: Execution Semanticsmentioning

confidence: 99%

“…[14] presents an early attempt to represent BPMN processes enriched with time features in Maude. This work was extended in [13] to provide automated verification of stochastic properties such as expected processing and synchronization times. Compared to these related works, the executable specification in rewriting logic presented in Section 3 can be seen as a semantic framework for BPMN, yet it is not the primary goal of this paper.…”

Section: Related Workmentioning

confidence: 99%

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A rewriting logic approach to resource allocation analysis in business process models

Durán

Rocha

Salaün

2019

Science of Computer Programming

Self Cite

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This paper presents an approach for the modeling and analysis of resource allocation for business processes. It enables the automatic computation of measures for precisely identifying and optimizing the allocation of resources in business processes, including resource usage over time. The proposed analysis, especially suited to support decision-making strategies, is illustrated with a case study of a parcel ordering and delivery by drones that is developed throughout the paper. The paper comprises an encoding of a significant and expressive subset of the Business Process Model and Notation (BPMN) in rewriting logic, an executable logic of concurrent change that can naturally deal with state and with concurrent computations. The encoding is by itself a formal semantics and interpreter of the BPMN subset that captures all concurrent behavior and thus is used to simulate the concurrent evolution of any business process with a given number of resources and replicas.

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Section: Execution Semanticsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A rewriting logic approach to resource allocation analysis in business process models

Durán

Rocha

Salaün

2019

Science of Computer Programming

Self Cite

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“…If the number of relations → is smaller than the number of activity X, then the weight is determined using Eq. (5). The result of the weighted graph is shown in the Fig.…”

Section: Heuristic Linear Temporal Logic (Hltl)mentioning

confidence: 99%

“…The deterministic model depicts all activity records. There are several deterministic process models, such as Data Flow Diagram (DFD) [3], Business Process Modeling Notation (BPMN) [4,5], Activity Diagram [6,7], and Linear Temporal Logic (LTL) [8,9]. The probabilistic model removes relationships of activity that have small occurrences in its model.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Linear Temporal Logic Pattern Algorithm in Business Process Model

Sungkono¹,

Rochmah²,

Sarno³

2019

IJIES

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Process discovery obtains a process model of activity records. There are two representations of process model, i.e. a probabilistic model and a deterministic model. A deterministic model takes all of activity records to depict a process model, however, the probabilistic model chooses several activity records that satisfy a threshold. Determination of the right threshold leads the emergence of many discovery algorithms of probabilistic models, such as Heuristic Miner, Fodina, Modified Heuristic Miner, and Modified Time-Based Heuristic Miners. Those algorithms determine a threshold based on users or an average of probabilities of activity records, so the quality of the model depends on user proficiency or frequent activities. This paper proposes a new algorithm of probabilistic model discovery, i.e. Heuristic Linear Temporal Logic (HLTL), which determines the threshold based on four quality aspects, i.e. Fitness, Precision, Generalization, and Simplicity. HLTL utilizes Linear Temporal Logic to create a formal representation of process model and store the weight of relationships used for the threshold formation. The result shows that the process model constructing by HLTL has better quality aspects than the process model constructing by Modified Heuristic Miners and Modified Time-Based Heuristic Miners. The generalization value of HLTL is 0.8422 and the generalization value of Modified Heuristic Miner and Modified Time-Based Heuristic Miners are 0.8421.

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“…In the present paper, it is also shown how to compute the variation between the minimum and maximum degree of parallelism of timed BPMN processes. More recently, the authors have developed a rewriting logic executable specification of BPMN with time and probabilities supporting the automatic analysis of stochastic properties via statistical model checking [6].…”

Section: Related Workmentioning

confidence: 99%