A Generic Approach to Extract Object-Centric Event Data from Relational Databases

Research Square

Abstract

Abstract Process mining provides a collection of techniques to gain insights into business processes by analyzing event logs.Organizations can gain various insights into their business processes by using process mining techniques.Such techniques use event logs extracted from relational databases supporting the business process as input.However, extracting event logs is challenging due to the size of the data, and it remains ad-hoc. Existing commercial tools partly support the extraction of event logs, but they are proprietary and focus on the mainstream processes such as Purchase-To-Pay (P2P) and Order-To-Cash (O2C).Moreover, the extracted event logs suffer from well-known deficiency, convergence, and divergence issues.For example, due to convergence events are unintentionally duplicated causing unreliable or confusing performance diagnostics.In this paper, we propose an approach to extract event logs for generic business processes while avoiding the aforementioned issues.More in detail, we extract object-centric event logs from a relational database by using an abstraction layer of the database, called Graph of Relationships (GoRs), designing blueprints with domain knowledge, and converting the database and blueprint into object-centric event logs.We fully implemented the proposed approach, can extract object-centric event logs from SAP ERP systems, and evaluate the utility and scalability of the proposed approach.

Authors 4

1. Alessandro Berti Aachen Process and Data Science Group

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

   Process and Data Science Group, RWTH Aachen University, Ahornstrasse 55, Aachen, 52074, NRW, Germany

   RWTH Aachen University Gyunam Park

   RWTH Aachen University Wil van der Aalst

2. Gyunam Park Aachen Process and Data Science Group

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

   Process and Data Science Group, RWTH Aachen University, Ahornstrasse 55, Aachen, 52074, NRW, Germany

   RWTH Aachen University Gyunam Park

   RWTH Aachen University Wil van der Aalst

3. Majid Rafiei Aachen Process and Data Science Group

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

   Process and Data Science Group, RWTH Aachen University, Ahornstrasse 55, Aachen, 52074, NRW, Germany

   RWTH Aachen University Gyunam Park

   RWTH Aachen University Wil van der Aalst

4. Wil M. P. van der Aalst Aachen Process and Data Science Group

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

   Process and Data Science Group, RWTH Aachen University, Ahornstrasse 55, Aachen, 52074, NRW, Germany

   RWTH Aachen University Gyunam Park

   RWTH Aachen University Wil van der Aalst