  Computer-based scientific experiments are becoming increasingly
data-intensive. High-Performance Computing (HPC) clusters are ideal for
executing large scientific experiment workflows. Executing large scientific
workflows in an HPC cluster leads to complex flows of data and control within
the system, which are difficult to analyze. This paper presents a case study
where process mining is applied to logs extracted from SLURM-based HPC
clusters, in order to document the running workflows and find the performance
bottlenecks. The challenge lies in correlating the jobs recorded in the system
to enable the application of mainstream process mining techniques. Users may
submit jobs with explicit or implicit interdependencies, leading to the
consideration of different event correlation techniques. We present a log
extraction technique from SLURM clusters, completed with an experimental.
