applied sciences Article Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems Ana Paji´ c Simovi´ c *, Sla ¯ dan Babarogi´ c, Ognjen Panteli´ c and Stefan Krstovi´ c   Citation: Paji´ c Simovi´ c, A.; Babarogi´ c, S.; Panteli´ c, O.; Krstovi´ c, S. Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems. Appl. Sci. 2021, 11, 5476. https://doi.org/10.3390/ app11125476 Academic Editors: Jaehun Park and Hyerim Bae Received: 30 April 2021 Accepted: 7 June 2021 Published: 12 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Faculty of Organizational Sciences, University of Belgrade, 11010 Belgrade, Serbia; sladjan.babarogic@fon.bg.ac.rs (S.B.); ognjen.pantelic@fon.bg.ac.rs (O.P.); stefan.krstovic@fon.bg.ac.rs (S.K.) * Correspondence: ana.pajic@fon.bg.ac.rs Abstract: Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format. Keywords: event log; transaction log; ERP system; artifact-centric approach; business document 1. Introduction At present, organizations use enterprise resource planning (ERP) systems as a core information and communications technologies (ICT) component. ERP systems encompass configurable modules supporting business processes that have received wide industrial adoption, and they contain extensive amounts of data about the behaviors of such processes. Recorded data can be used to analyze whether a predefined process specification conforms with real activities [1]. Process mining offers automated techniques for this type of analysis. Process mining has emerged in the past few years as a new analytical discipline that focuses on extracting insights about processes from the event data stored in information systems. Process mining provides the techniques to automatically discover process models from data, find the mismatch between process models and process executions, and improve models based on information obtained from their past executions [2]. These techniques rely on the existence of an event log whose structure is suitable for mining. An event log assumes that events that refer to an activity occur at a particular time in precisely one case [3]. While event logs structured in this form are mostly available in process-aware information systems, such a structure is not explicitly generated by ERP systems and other domain-specific business systems [4–6]. These systems record events implicitly, separately, and without a common case identifier. Thus, such logs need to be generated from the data that typically exist in a relational database. To build such an event log is the key enabler for process mining and is a complex task [7,8]. To extract data from ERP systems in an event log format suitable for process mining, several challenges need to be addressed. First, there is not one possible case notion, but Appl. Sci. 2021, 11, 5476. https://doi.org/10.3390/app11125476 https://www.mdpi.com/journal/applsci