Stochastic Process Model-Log Quality Dimensions: An Experimental Study

Burke, Adam; Leemans, Sander J. J.; Wynn, Moe Thandar; Aalst, Wil M. P. van der; Hofstede, Arthur H. M. ter

doi:10.1109/icpm57379.2022.9980707

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Leemans et al [12,13] and Burke et al [5] studied the evaluation of stochastic models using, among other measures, Earth Movers' Distance. However, they focus solely on the control-flow perspective, and their purpose is mainly conformance checking.…”

Section: Quality Measures For Business Process Simulation Modelsmentioning

confidence: 99%

Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models

Chapela-Campa,

Benchekroun,

Baron

et al. 2023

Lecture Notes in Computer Science

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Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate what would be the cycle time of a process if one or more resources became unavailable. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BPS models may be manually designed, based on information collected from stakeholders and empirical observations, or automatically discovered from execution data. Regardless of its origin, a key question when using a BPS model is how to assess its quality. In this paper, we propose a collection of measures to evaluate the quality of a BPS model w.r.t. its ability to replicate the observed behavior of the process. We advocate an approach whereby different measures tackle different process perspectives. We evaluate the ability of the proposed measures to discern the impact of modifications to a BPS model, and their ability to uncover the relative strengths and weaknesses of two approaches for automated discovery of BPS models. The evaluation shows that the measures not only capture how close a BPS model is to the observed behavior, but they also help us to identify sources of discrepancies.

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Section: Quality Measures For Business Process Simulation Modelsmentioning

confidence: 99%

Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models

Chapela-Campa,

Benchekroun,

Baron

et al. 2023

Lecture Notes in Computer Science

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show abstract

“…Stochastic process-based models have been studied extensively in literature. In the context of this work, we are interested in formal, Petri net-based stochastic models that are at the basis of the recent series of approaches in stochastic process discovery [12,53,14] and conformance checking [32,36,8,3,15]. Such approaches all refer to the model of (generalised) stochastic Petri nets, or fragments thereof.…”

Section: Related Workmentioning

confidence: 99%

Reasoning on Labelled Petri Nets and Their Dynamics in a Stochastic Setting

Leemans

Maggi

Montali

2022

Lecture Notes in Computer Science

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Capturing stochastic behaviors in business and work processes is essential to quantitatively understand how nondeterminism is resolved when taking decisions within the process. This is of special interest in process mining, where event data tracking the actual execution of the process are related to process models, and can then provide insights on frequencies and probabilities. Variants of stochastic Petri nets provide a natural formal basis to represent stochastic behaviors and support different data-driven and model-driven analysis tasks in this spectrum. However, when capturing processes, such nets need to be labelled with (possibly duplicated) activities, and equipped with silent transitions that model internal, non-logged steps related to the orchestration of the process. At the same time, they have to be analyzed in a finite-trace semantics, matching the fact that each process execution consists of finitely many steps. These two aspects impede the direct application of existing techniques for stochastic Petri nets, calling for a novel characterization that incorporates labels and silent transitions in a finite-trace semantics. In this article, we provide such a characterization starting from generalized stochastic Petri nets and obtaining the framework of labelled stochastic processes (LSPs). On top of this framework, we introduce different key analysis tasks on the traces of LSPs and their probabilities. We show that all such analysis tasks can be solved analytically, in particular reducing them to a single method that combines automata-based techniques to single out the behaviors of interest within a LSP,

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Automated generation of process simulation scenarios from declarative control-flow changes

Barón-Espitia,

Dumas,

González-Rojas

2024

PeerJ Computer Science

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Business process simulation is an established approach to estimate the potential impact of hypothetical changes on a process, particularly in terms of time and cost-related performance measures. To overcome the complexity associated with manually specifying and fine-tuning simulation models, data-driven simulation (DDS) methods enable users to discover accurate business process simulation models from event logs. However, in the pursuit of accuracy, DDS methods often generate overly complex models. This complexity can hinder analysts when attempting to manually adjust these models to represent what-if scenarios, especially those involving control-flow changes such as activity re-sequencing. This article addresses this limitation by proposing an approach that allows users to specify control-flow changes to a business process simulation model declaratively, and to automate the generation of what-if scenarios. The proposed approach employs a generative deep learning model to produce traces resembling those in the original log while implementing the user-specified control-flow changes. Subsequently, the technique generates a stochastic process model, and uses it as a basis to construct a modified simulation model for what-if analysis. Experiments show that the simulation models generated through this approach replicate the accuracy of models manually created by directly altering the original process model.

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Stochastic Process Model-Log Quality Dimensions: An Experimental Study

Cited by 3 publications

References 20 publications

Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models

Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models

Reasoning on Labelled Petri Nets and Their Dynamics in a Stochastic Setting

Automated generation of process simulation scenarios from declarative control-flow changes

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