Step-by-Step Case ID Identification Based on Activity Connection for Cross-Organizational Process Mining

Abstract

Cross-organizational process mining aims to discover an entire process model across multiple organizations where their identifier (ID) systems are not managed uniformly, and each organization has an independent ID system. Cross-organizational process mining has been gaining popularity as information systems increase in complexity. However, previous methods have limitations in that they do not work well for event logs that contain only common items, or cyclic orchestrations, which indicates that the model contains loops. In this paper, we propose an accurate cross-organizational process mining technique based on a step-by-step case ID identification mechanism that uses only common items in event logs and can handle cyclic orchestrations. Step-by-step case ID identification repeats the following steps: 1) identification of case IDs based on activity connection of adjacent event pairs, and 2) extraction of additional activity connections by leveraging the newly identified case IDs. We alternately identify the most probable case ID pairs and remove events belonging to these identified case IDs from the event log, which contributes to extracting additional activity connections and narrowing down the candidates of case ID pairs. Evaluation using real-world event logs showed that the proposed method generates the process model with more than 98.4% precision and more than 94.2% recall for two datasets, outperforming previous methods.