The use of process mining in a business process simulation context: Overview and challenges

Martin, Niels; Depaire, Benoît; Caris, An

doi:10.1109/cidm.2014.7008693

Cited by 18 publications

(13 citation statements)

References 24 publications

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“…7 Simod takes as input an event log in XES format, and produces as output a BPMN model that includes simulation parameters, ready to be simulated using the BIMP simulator [5]. 8 The source code of Simod can also be configured to produce models for the Scylla simulator [6], but BIMP is used as the default simulator because Scylla only supports a restricted set of probability distributions, thus restricting the space of configuration options.…”

Section: Discussionmentioning

confidence: 99%

Automated discovery of business process simulation models from event logs

Camargo

Dumas

González-Rojas

2020

Decision Support Systems

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Simulation is a versatile technique for quantitative analysis of business processes. It allows analysts to estimate the performance of a process under multiple scenarios. However, the discovery, validation, and tuning of business process simulation models is cumbersome and error-prone. It requires manual iterative refinement of the process model and simulation parameters in order to match the observed reality as closely as possible. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is then used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains.

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Section: Discussionmentioning

confidence: 99%

Automated discovery of business process simulation models from event logs

Camargo

Dumas

González-Rojas

2020

Decision Support Systems

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“…They use an ABS model for validation of their work. Martin et al (2014) consider the use of process mining in a simulation context. They foresee the potential of process mining for retrieving insights on which activities are executed under which circumstances.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Using Agent-Based Simulation for Emergent Behavior Detection in Cyber-Physical Systems

Bemthuis

Mes

Iacob

et al. 2020

2020 Winter Simulation Conference (WSC)

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Traditional modeling approaches, based on predefined business logic, offer little support for today's complex environments. In this paper, we propose a conceptual agent-based simulation framework to help not only discover complex business processes but also to analyze and learn from emergent behavior arising in cyber-physical systems. Techniques originating from agent-based modeling as well as from the process mining discipline are used to reinforce agent-based decision-making. Whereas agent-technology is used to orchestrate the integration and relationship between the environment and business logic activities, process mining capabilities are mainly used to discover and analyze emergent behavior. Using a functional decomposition approach, we specified three agent types: cyber-physical controller agent, business rule management agent, and emergent behavior detection agent. We use agent-based simulation of a logistics cold chain case study to demonstrate the feasibility of our approach.

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“…Although there is a need for general purpose BP simulation models, there are no methods to discover fully capable simulation models from event logs, which would be detailed enough to use data in BPs for the control-flow execution, decisions and resource allocations. On the other hand, there are multiple methods to discover such parts or parameters of simulation models [4]. This section provides an overview of related works on process mining and simulation to facilitate BP analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Process mining techniques use the data saved in IS regarding BP historical execution and facilitate discovery of business process models, to validate and improve existing business models or make general insights on the performance of business processes. Although there are ways on how to employ process mining techniques for discovery of simulation model parameters [4], there are no approaches on how to automatically generate BP simulation models from event logs.…”

Section: Introductionmentioning

confidence: 99%

“…The generated simulation model re-uses probabilistic model to predict the execution sequence of the process and generate the process data. The model can further be customised by an analyst to restrict pathways, manipulate process execution, add additional execution rules and in general perform what-if analysis that is usually done during BP simulation [4]. The approach is evaluated using conformance techniques to test whether the automatically created simulation models, once simulated, conform to the processes originating from the source event logs using process mining discovery and conformance techniques.…”

Section: Introductionmentioning

confidence: 99%

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An approach to business process simulation using mined probabilistic models

Savickas

Vasilecas

2018

COMSIS J

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There are many approaches on how to analyse business processes, but the simulation is still not widely employed due to high costs associated with simulation model creation. In this paper, an approach on how to automatically generate dynamic business process simulation model is presented. The approach discovers belief network of the process from an event log and uses it to generate a simulation model automatically. Such model then can be further customised to facilitate analysis. For evaluation of the approach, conformance of the simulation results with the source event logs was calculated. The simulation results were event logs that were generated during the simulation of the discovered models. The evaluation showed that the approach could be used for initial simulation model generation.

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The use of process mining in a business process simulation context: Overview and challenges

Cited by 18 publications

References 24 publications

Automated discovery of business process simulation models from event logs

Automated discovery of business process simulation models from event logs

Using Agent-Based Simulation for Emergent Behavior Detection in Cyber-Physical Systems

An approach to business process simulation using mined probabilistic models

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