Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding

Sim, Sunghyun; Sutrisnowati, Riska Asriana; Won, Seokrae; Lee, Sang-Hwa; Bae, Hyerim

doi:10.1109/access.2022.3143609

Cited by 4 publications

(2 citation statements)

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“…Furthermore, the event log generation procedure may involve multiple databases from different back-end systems [10]. A high precision achieved by a process mining method is related to the quality of the event log used [11]. Therefore, validating and verifying event data conversion to event logs is essential to guarantee reliable results.…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches are available in state-of-the-art to support the generation of event logs: in a semi-automatic way from relational databases [14], derive process activities through a method based on natural language inference [15], from a real-world data warehouse of a healthcare system [16], from database redo logs [17,18], from data stored on the blockchain [19], and conversion of event data to event logs using deep learning [11]. An event data source that has provoked great interest in the process mining community is information systems based on Internet of Things (IoT) technology [20].…”

Section: Introductionmentioning

confidence: 99%

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Semi-Automated Approach for Building Event Logs for Process Mining from Relational Database

et al. 2022

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Process mining is a novel alternative that uses event logs to discover, monitor, and improve real business processes through knowledge extraction. Event logs are a prerequisite for any process mining technique. The extraction of event data and event log building is a complex and time-intensive process, with human participation at several stages of the procedure. In this paper, we propose a framework to semi-automatically build an event log based on the XES standard from relational databases. The framework comprises the stages of requirements identification, event log construction, and event log evaluation. In the first stage, the data is interpreted to identify the relationship between the columns and business process activities, then the business process entities are defined. In the second stage, the hierarchical structure of the event log is specified. Likewise, a formal rule set is defined to allow mapping the database columns with the attributes specified in the event log structure, enabling the extraction of attributes. This task is implemented through a correlation method at the case, event, and activity levels, to automatic event log generation. We validate the event log through quality metrics, statistical analysis, and business process discovery. The former allows for determining the quality of the event log built using the metrics of accuracy, completeness, consistency, and uniqueness. The latter evaluates the business process models discovered through precision, coverage, and generalization metrics. The proposed approach was evaluated using the autonomous Internet of Things (IoT) air quality monitoring system’s database and the patient admission and healthcare service delivery database, reaching acceptable values both in the event log quality and in the quality of the business process models discovered.

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Section: Introductionmentioning

confidence: 99%