Discovery and Simulation of Data-Aware Business Processes
This addresses the gap in business process simulation for organizations needing more accurate predictions, though it is incremental as it extends existing methods to include data perspectives.
The paper tackled the problem of business process simulation models neglecting data attributes, which affect activity execution, by introducing a data-aware modeling approach and discovery method from event logs. The result was that the method accurately identifies data attribute types and update rules, and the models more closely replicate control flow compared to data-unaware models.
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.