Flattening Event Data Extracted From SAP For Process Discovery

Cookland, James (2023) Flattening Event Data Extracted From SAP For Process Discovery. Masters thesis, Dublin, National College of Ireland.

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Abstract

Extracting and mining event log data from the SAP database architecture is a hot topic in the field of process mining. The object-centric nature of the SAP database architecture often leads to challenging problems. The most prominent of which being convergence and divergence present in the event data. This research will explore the challenges associated with extracting, preparing and modeling event data from a technology distribution business, which uses SAP as its Enterprise Resource Planning software. Resulting in an end-to-end exploration of a process mining endeavor using event data extracted from SAP. The business case used for this research is that of a bespoke purchasing process that includes a crossover between the EINKBELEG and VERKBELEG object classes in the SAP change log tables. Additionally, this research will discuss methods of clustering, that add weight to specific activity notions by either increasing or decreasing activity notion granularity. This method effectively highlights the specific process activities that a researcher or business would like to see in the final process model.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Zahid Iqbal, Muhammad UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	17 May 2023 16:39
Last Modified:	17 May 2023 16:39
URI:	https://norma.ncirl.ie/id/eprint/6581

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