Abstract
When aggregating logistic event data from different supply chain actors and information systems for process mining, interoperability, data loss, and data quality are common challenges. This position paper proposes and evaluates the use of the Open Trip Model (OTM) for process mining. Inspired by the current industrial use of the OTM for reporting and business intelligence, we believe that the data model of OTM can be utilized for unified storage, integration, interoperability, and querying of logistic event data. Therefore, the OTM data model is mapped to a generic event log structure to satisfy the minimum requirements for process mining. A demonstrative scenario is used to show how event data can be extracted from the OTM’s default scenario dataset to create an event log as the starting point for process mining. Thus, this approach provides a foundation for future research about interoperability challenges and unifying process mining models based on industry standards, and a startin g point for developing process mining applications in the logistics industry.
Original language | English |
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Title of host publication | Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS) |
Publisher | SCITEPRESS |
Pages | 290-296 |
Number of pages | 7 |
Volume | 1 |
ISBN (Electronic) | 978-989-758-509-8 |
ISBN (Print) | 978-989-758-509-8 |
DOIs | |
Publication status | Published - 28 Apr 2021 |
Event | 23rd International Conference on Enterprise Information Systems, ICEIS 2021 - Online Conference Duration: 26 Apr 2021 → 28 Apr 2021 Conference number: 23 http://www.iceis.org/Home.aspx |
Conference
Conference | 23rd International Conference on Enterprise Information Systems, ICEIS 2021 |
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Abbreviated title | ICEIS 2021 |
City | Online Conference |
Period | 26/04/21 → 28/04/21 |
Internet address |
Keywords
- Open Trip Model
- Process Mining
- Logistics
- Event Data