A domain-specific language for supporting event log extraction from ERP systems

Simovic, Ana Pajic; Babarogić, Slađan; Pantelić, Ognjen

doi:10.1109/icccc.2018.8390430

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…To deal with one-to-many and many-to-many relations, the metamodel proposed in this paper provides an artifact-centric view of databases. The DSML metamodel was initially described in our previous work [30]; however, this metamodel was revised and we acknowledged some limitations compared to the new metamodel proposed in this paper. Firstly, the metamodel was incomplete in terms of its semantics and also inconsistent that made it more difficult to understand by the end-user.…”

Section: Discussionmentioning

confidence: 99%

Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

2021

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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.

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Section: Discussionmentioning

confidence: 99%

Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

2021

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“…-Extracting object-centric event logs from information systems: This includes several contributions related to the storage format and some work on the extraction from SAP logs or ERP systems in general [15,34,48]. -Discovering process models from object-centric event logs: Artifact-centric modeling is an approach to model processes with multiple case notions by combining process and data [22,35].…”

Section: Object-centric Process Miningmentioning

confidence: 99%

Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP)

Aalst¹,

Brockhoff²,

Ghahfarokhi³

et al. 2021

Preprint

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Operational processes in production, logistics, material handling, maintenance, etc., are supported by cyber-physical systems combining hardware and software components. As a result, the digital and the physical world are closely aligned, and it is possible to track operational processes in detail (e.g., using sensors). The abundance of event data generated by today's operational processes provides opportunities and challenges for process mining techniques supporting process discovery, performance analysis, and conformance checking. Using existing process mining tools, it is already possible to automatically discover process models and uncover performance and compliance problems. In the DFGfunded Cluster of Excellence "Internet of Production" (IoP), process mining is used to create "digital shadows" to improve a wide variety of operational processes. However, operational processes are dynamic, distributed, and complex. Driven by the challenges identified in the IoP cluster, we work on novel techniques for comparative process mining (comparing process variants for different products at different locations at different times), object-centric process mining (to handle processes involving different types of objects that interact), and forward-looking process mining (to explore "What if?" questions). By addressing these challenges, we aim to develop valuable "digital shadows" that can be used to remove operational friction.

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Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP)

Aalst

Brockhoff

Ghahfarokhi

et al. 2021

Communications in Computer and Information Science

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A domain-specific language for supporting event log extraction from ERP systems

Cited by 6 publications

References 14 publications

Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP)

Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP)

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