Extracting Event Data from Document-Driven Enterprise Systems

Proc. of the 35th Int. Conf. on Advanced Information Systems Engineering (CAiSE 2023). Volume 13901 of Lecture Notes in Computer Science. 2023.

The preparation of input event data is one of the most critical phases in process mining projects. Different frameworks have been developed to offer methodologies and/or supporting toolkits for data preparation. One of these frameworks, called OnProm, relies on sophisticated semantic technologies to extract event logs from relational databases. The toolkit consists of a series of general steps, meant to work on arbitrary, legacy databases. However, in many settings, the input database is not a legacy one but is structured with conceptually understandable object types and relationships that can be effectively employed to support business users in the extraction process. This is, for example, the case for document-driven enterprise systems. In this paper, we focus on this class of systems and propose a guided approach, erprep, to support a group of business and technical users in setting up OnProm with minimal effort. We demonstrate the approach in a real-life use case.

@inproceedings{CAiSE-2023,
   title = "Extracting Event Data from Document-Driven Enterprise Systems",
   year = "2023",
   author = "Diego Calvanese and Mieke Jans and Kalayci, Tahir Emre and
Marco Montali",
   booktitle = "Proc. of the 35th Int. Conf. on Advanced Information Systems
Engineering (CAiSE 2023)",
   pages = "193--209",
   volume = "13901",
   publisher = "Springer",
   series = "Lecture Notes in Computer Science",
   doi = "10.1007/978-3-031-34560-9_12",
}