Process mining empowers companies to optimise operational processes by deriving insights from event logs. It involves assessing event logs or the resultant process models against existing models and identifying challenges within executed processes to enhance their efficiency. However, a critical prerequisite for effective process mining is data cleaning, which simplifies the complexities inherent in real-world event data, making it amenable to comprehension, processing, and leveraging with process mining techniques. Consequently, new methodologies and approaches for event data pre-processing have garnered significant attention within the business process scientific community. This paper presents a systematic literature review summarising relevant techniques for event data pre-processing in the context of business process mining. The objective of this study is to categorise approaches and methodologies related to event data pre-processing while shedding light on the crucial issues associated with these techniques.

Zineb Lamghari

ASM Science Journal

The ASM Science Journal publishes advancements in the broad fields of medical, engineering, earth, mathematical, physical, chemical and agricultural sciences as well as ICT. Scientific articles published will be on the basis of originality, importance and significant contribution to science, scientific research and the public.     Scientific articles published will be on the basis of originality, importance and significant contribution to science, scientific research and the public. Scientists who subscribe to the fields listed above will be the source of papers to the journal. All articles will be reviewed by at least two experts in that particular field.

The ASM Science Journal publishes the latest research advancements in the fields of medical, engineering, earth, mathematical, physical, chemical, and agricultural sciences, as well as information and communication technology (ICT). All published scientific articles must demonstrate originality, significance, and a substantial contribution to science, scientific research, and the public. The papers submitted to the journal are sourced from scientists in the aforementioned fields, and all articles will be reviewed by at least two experts in the respective field.

ASM科学期刊发表医学、工程、地球、数学、物理、化学和农业科学以及信息与通信技术（ICT）领域的最新研究进展。所有发表的科学文章必须具备原创性、重要性和对科学、科研及公众的显著贡献。该期刊的论文来源于上述领域的科学家，所有文章将由至少两位该领域的专家进行审稿。

Exploring Event Data Pre-processing Approaches for Business Process Mining

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