An ontology-based method for improving the quality of process event logs using database bin logs. (25th May 2021)

• Record Type: Journal Article
• Authors: Ghalibafan, Shokoufeh; Behkamal, Behshid; Kahani, Mohsen; Allahbakhsh, Mohammad
• Is Part Of: International journal of metadata, semantics and ontologies. Volume 14:Number 4(2020)
• Page Start: 279
• Page End: 289
• Publication Date: 2021-05-25
• Subjects: data quality -- process mining -- event log -- ontology matching -- database bin log; Metadata -- Periodicals; Semantic Web -- Periodicals; Ontologies (Information retrieval) -- Periodicals; Data structures (Computer science) -- Periodicals; Information theory -- Periodicals; 005.74
• Languages: English
• ISSNs: 1744-2621

Abstract

The main goal of process mining is discovering models from event logs. The usefulness of these discovered models is directly related to the quality of event logs. Researchers proposed various solutions to detect deficiencies and improve the quality of event logs; however, only a few have considered the application of a reliable external source for the improvement of the quality of event data. In this paper, we propose a method to repair the event log using the database bin log. We show that database operations can be employed to overcome the inadequacies of the event logs, including incorrect and missing data. To this end, we, first, extract an ontology from each of the event logs and the bin log. Then, we match the extracted ontologies and remove inadequacies from the event log. The results show the stability of our proposed model and its superiority over related works.