CoSMo: a Framework to Instantiate Conditioned Process Simulation Models
This work addresses the problem of performing what-if analysis in business process simulation for practitioners by incorporating a-priori knowledge into deep learning models, though it is incremental as it builds on existing techniques.
The paper tackles the challenge of integrating user-defined constraints into deep learning models for business process simulation, introducing CoSMo, a recurrent neural architecture that simulates event logs adhering to specific declarative rules, with experimental validation showing its efficacy in handling control-flow and data-flow perspectives.
Process simulation is gaining attention for its ability to assess potential performance improvements and risks associated with business process changes. The existing literature presents various techniques, generally grounded in process models discovered from event log data or built upon deep learning algorithms. These techniques have specific strengths and limitations. Traditional data-driven approaches offer increased interpretability, while deep learning-based excel at generalizing changes across large event logs. However, the practical application of deep learning faces challenges related to managing stochasticity and integrating information for what-if analysis. This paper introduces a novel recurrent neural architecture tailored to discover COnditioned process Simulation MOdels (CoSMo) based on user-based constraints or any other nature of a-priori knowledge. This architecture facilitates the simulation of event logs that adhere to specific constraints by incorporating declarative-based rules into the learning phase as an attempt to fill the gap of incorporating information into deep learning models to perform what-if analysis. Experimental validation illustrates CoSMo's efficacy in simulating event logs while adhering to predefined declarative conditions, emphasizing both control-flow and data-flow perspectives.