Optimal alignments between large event logs and process models over distributed systems: An approach based on Petri nets

Abstract

Process descriptions are the backbones for creating products and delivering services automatically. Computing the alignments between process descriptions (such as process models) and process behavior is one of the fundamental tasks to lead to better processes and services. The reason is that the computed results can be directly used in checking compliance, diagnosing deviations, and analyzing bottlenecks for processes. Although various alignment techniques have been proposed in recent years, their performance is still challenged by large logs and models. In this work, we introduce an efficient approach to accelerate the computation of alignments. Specifically, we focus on the computation of optimal alignments, and try to improve the performance of the state-of-the-art A∗-based method through Petri net decomposition. We present the details of our designs and also show that our approach can be easily implemented in a distributed environment using the Spark platform. Using datasets with large event logs and process models, we experimentally demonstrate that our approach can indeed accelerate current A∗-based implementations in general.