Insufficient Data PDF: A Quality-Aware Approach for Extracting Event Logs from Relational Data

Process mining has become a crucial tool in various industries, enabling organizations to analyze and improve their business processes. However, extracting event logs from relational data is often a time-consuming and labor-intensive task that requires manual pre-processing. This process can be challenging, especially when dealing with large datasets or complex relationships between different tables. In this article, we will discuss the importance of quality-aware approaches for extracting event logs from relational data and introduce RDB2Log, a semi-automated approach designed to address these challenges.

Traditional methods for extracting event logs involve manual pre-processing, which can be prone to errors and inconsistencies. This process requires analysts to have extensive domain knowledge and skills, making it difficult to extract high-quality event logs. Moreover, the quality of input data is critical in generating accurate and reliable results in process mining. Therefore, a framework that ensures data quality and efficiency is essential.

RDB2Log is a quality-aware approach designed to address these challenges by providing a semi-automated solution for extracting event logs from relational data. This approach involves validating RDB2Log's design against established design objectives extracted from literature and competing artifacts. The design and performance of the approach were evaluated with process mining experts, and a prototype was implemented with a defined set of quality metrics.

In laboratory settings and real-world case studies, we applied RDB2Log to demonstrate its effectiveness in extracting high-quality event logs from relational data. Our results show that this approach can significantly reduce manual pre-processing time while ensuring the accuracy and reliability of extracted event logs.

The importance of data quality in process mining cannot be overstated. A fit-for-purpose event log is nearly always required, which necessitates time-consuming manual pre-processing to extract events from source data. The quality of input data directly affects the accuracy and reliability of results generated by process mining techniques. Therefore, a framework that ensures data quality is critical for process mining.

In conclusion, RDB2Log offers a semi-automated approach for extracting event logs from relational data, ensuring high-quality output while reducing manual pre-processing time. This approach addresses the challenges associated with traditional methods and provides a valuable tool for organizations seeking to improve their business processes through process mining.

Insufficient data pdf