A Scalable Database for the Storage of Object-Centric Event Logs

Berti, Alessandro; Ghahfarokhi, Anahita Farhang; Park, Gyunam; Aalst, Wil M. P. van der

doi:10.48550/arxiv.2202.05639

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“…Each row shows an event that has an identifier, a timestamp, an activity, related objects, and attributes. objects interact with each other in a process [3,4,11,12,27], for example, considering a Purchase-to-Pay (P2P) process where orders, items, and customers are involved [14,13]. Several process discovery techniques have been developed on top of event logs with multiple case notions [2,8,17,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Clustering Object-Centric Event Logs

Ghahfarokhi¹,

Akoochekian²,

Zandkarimi³

et al. 2022

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Process mining provides various algorithms to analyze process executions based on event data. Process discovery, the most prominent category of process mining techniques, aims to discover process models from event logs, however, it leads to spaghetti models when working with real-life data. Therefore, several clustering techniques have been proposed on top of traditional event logs (i.e., event logs with a single case notion) to reduce the complexity of process models and discover homogeneous subsets of cases. Nevertheless, in real-life processes, particularly in the context of Business-to-Business (B2B) processes, multiple objects are involved in a process. Recently, Object-Centric Event Logs (OCELs) have been introduced to capture the information of such processes, and several process discovery techniques have been developed on top of OCELs. Yet, the output of the proposed discovery techniques on real OCELs leads to more informative but also more complex models. In this paper, we propose a clustering-based approach to cluster similar objects in OCELs to simplify the obtained process models. Using a case study of a real B2B process, we demonstrate that our approach reduces the complexity of the process models and generates coherent subsets of objects which help the end-users gain insights into the process.

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