Probabilistic Model Checking of BPMN Processes at Runtime

Falcone, Ylìès; Salaün, Gwen; Zuo, Ahang

doi:10.1007/978-3-031-07727-2_11

Cited by 10 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All the steps of our approach have been implemented in the WEASY tool, which was successfully applied to a series of examples for validation purposes. The main perspective of this work aims at enlarging the considered BPMN subset to take additional constructs into account, such as data, conditions or probabilities in exclusive gateways, see [1,2,4] for instance.…”

Section: Discussionmentioning

confidence: 99%

WEASY: A Tool for Modelling Optimised BPMN Processes

Contreras

Falcone

Salaün

et al. 2022

Formal Aspects of Component Software

Self Cite

View full text Add to dashboard Cite

Business Process Model and Notation (BPMN) is a standard modelling language for workflow-based processes. Building an optimised process with this language is not easy for non-expert users due to the lack of support at design time. This paper presents a lightweight modelling tool to support such users in building optimised processes. First, the user defines the tasks involved in the process and possibly gives a partial order between tasks. The tool then generates an abstract graph, which serves as a simplified version of the process being specified. Next, the user can refine this graph using the minimum and maximum execution time of the whole graph computed by the tool. Once the user is satisfied with a specific abstract graph, the tool synthesises a BPMN process corresponding to that graph. Our tool is called WEASY and is available as an open-source web application.

show abstract

Section: Discussionmentioning

confidence: 99%

WEASY: A Tool for Modelling Optimised BPMN Processes

Contreras

Falcone

Salaün

et al. 2022

Formal Aspects of Component Software

Self Cite

View full text Add to dashboard Cite

show abstract

“…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 3.3.…”

Section: Fig 3 Representation Of the Simulation Objectmentioning

confidence: 99%

“…Four different strategies were implemented and compared from an experimental perspective. [14] presents an approach to perform probabilistic model checking of multiple executions of a BPMN process (including time and resources) at runtime.…”

Section: Related Workmentioning

confidence: 99%

From Static to Dynamic Analysis and Allocation of Resources for BPMN Processes

Durán

Falcone

Rocha

et al. 2022

Rewriting Logic and Its Applications

Self Cite

View full text Add to dashboard Cite

Business process optimisation is a strategic activity in organisations because of its potential to increase profit margins and reduce operational costs. One of the main challenges in this context is concerned with the problem of optimising the allocation and sharing of resources. In this work, processes are described using the BPMN notation extended with an explicit description of execution time and resources associated with tasks, and can be concurrently executed multiple times. First, a simulation-based approach for computing certain metrics of interest, such as average execution time or resource usage, is presented. This approach applies off-line and is static in the sense that the number of resources does not evolve over the time of the simulation. In a second step, an alternative approach is presented, which works online, thus requiring the instrumentation of an existing platform for retrieving information of interest during the processes' execution. This second approach is dynamic because the number of resource replicas is updated over the time of the execution. This paper aims at stressing pros and cons of both approaches, and at showing how they complement each other.

show abstract

“…In recent times, probabilistic model checking has established itself as a powerful method for verifying both hardware and software. This technique is recognised for its broad applications, notably in the assessment of reliability, dependability, and performance [19,20]. A noteworthy collaboration was achieved by combining probabilistic model checking and process mining.…”

Section: Introductionmentioning

confidence: 99%

Statistical Model Checking in Process Mining: A Comprehensive Approach to Analyse Stochastic Processes

Mangi,

Su,

Zhang

2023

Future Internet

View full text Add to dashboard Cite

The study of business process analysis and optimisation has attracted significant scholarly interest in the recent past, due to its integral role in boosting organisational performance. A specific area of focus within this broader research field is process mining (PM). Its purpose is to extract knowledge and insights from event logs maintained by information systems, thereby discovering process models and identifying process-related issues. On the other hand, statistical model checking (SMC) is a verification technique used to analyse and validate properties of stochastic systems that employs statistical methods and random sampling to estimate the likelihood of a property being satisfied. In a seamless business setting, it is essential to validate and verify process models. The objective of this paper is to apply the SMC technique in process mining for the verification and validation of process models with stochastic behaviour and large state space, where probabilistic model checking is not feasible. We propose a novel methodology in this research direction that integrates SMC and PM by formally modelling discovered and replayed process models and apply statistical methods to estimate the results. The methodology facilitates an automated and proficient evaluation of the extent to which a process model aligns with user requirements and assists in selecting the optimal model. We demonstrate the effectiveness of our methodology with a case study of a loan application process performed in a financial institution that deals with loan applications submitted by customers. The case study highlights our methodology’s capability to identify the performance constraints of various process models and aid enhancement efforts.

show abstract

Probabilistic Model Checking of BPMN Processes at Runtime

Cited by 10 publications

References 20 publications

WEASY: A Tool for Modelling Optimised BPMN Processes

WEASY: A Tool for Modelling Optimised BPMN Processes

From Static to Dynamic Analysis and Allocation of Resources for BPMN Processes

Statistical Model Checking in Process Mining: A Comprehensive Approach to Analyse Stochastic Processes

Contact Info

Product

Resources

About