2024/08/24 by Orlenys López‐Pintado, López-Pintado, Orlenys, Serhii Murashko +3 · 2 citations
Business, Management and Accounting · Computer Science · #Advanced Database Systems and Queries #Business Process Modeling and Analysis #D.2.9 #FOS: Computer and information sciences #H.4.1 #Machine Learning (cs.LG) #Service-Oriented Architecture and Web Services #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2408.13666
openalex publication_date 2024/08/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Simulation is a common approach to predict the effect of business process changes on quantitative performance. The starting point of Business Process Simulation (BPS) is a process model enriched with simulation parameters. To cope with the typically large parameter spaces of BPS models, several methods have been proposed to automatically discover BPS models from event logs. Virtually all these approaches neglect the data perspective of business processes. Yet, the data attributes manipulated by a business process often determine which activities are performed, how many times, and when. This paper addresses this gap by introducing a data-aware BPS modeling approach and a method to discover data-aware BPS models from event logs. The BPS modeling approach supports three types of data attributes (global, case-level, and event-level) as well as deterministic and stochastic attribute update rules and data-aware branching conditions. An empirical evaluation shows that the proposed method accurately discovers the type of each data attribute and its associated update rules, and that the resulting BPS models more closely replicate the process execution control flow relative to data-unaware BPS models.