EconPapers    
Economics at your fingertips  
 

Feature extraction from event logs for predictive monitoring of business processes

Florian Borchert

No y6px4, Thesis Commons from Center for Open Science

Abstract: In this master’s thesis, we investigate feature extraction techniques for event log traces. We provide an overview over the field of predictive monitoring and analyze existing trace profiles proposed in the literature. Based on this, we apply the results obtained in recent research on process discovery to find meaningful abstractions over related subsequences of events. We assess different feature sets by evaluating their predictive power in a supervised classification setting as well as a semi-supervised outlier detection setting. For this purpose, we use two datasets from public administration, describing complex processes in EU agricultural subsidy management. Our particular goal in this domain is to predict negative outcomes, for instance, additional work due to legal claims or corrections necessary after initial payment decisions.

Date: 2017-10-30
References: View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://osf.io/download/5b083f0d8811f300127d1e7b/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:thesis:y6px4

DOI: 10.31219/osf.io/y6px4

Access Statistics for this paper

More papers in Thesis Commons from Center for Open Science
Bibliographic data for series maintained by OSF ().

 
Page updated 2020-01-17
Handle: RePEc:osf:thesis:y6px4