  Process mining is an area of research that supports discovering information
about business processes from their execution event logs. The increasing amount
of event logs in organizations challenges current process mining techniques,
which tend to load data into the memory of a computer. This issue limits the
organizations to apply process mining on a large scale and introduces risks due
to the lack of data management capabilities. Therefore, this paper introduces
and formalizes a new approach to store and retrieve event logs into/from graph
databases. It defines an algorithm to compute Directly Follows Graph (DFG)
inside the graph database, which shifts the heavy computation parts of process
mining into the graph database. Calculating DFG in graph databases enables
leveraging the graph databases' horizontal and vertical scaling capabilities in
favor of applying process mining on a large scale. Besides, it removes the
requirement to move data into analysts' computer. Thus, it enables using data
management capabilities in graph databases. We implemented this approach in
Neo4j and evaluated its performance compared with current techniques using a
real log file. The result shows that our approach enables the calculation of
DFG when the data is much bigger than the computational memory. It also shows
better performance when dicing data into small chunks.
