  Automated process discovery is a class of process mining methods that allow
analysts to extract business process models from event logs. Traditional
process discovery methods extract process models from a snapshot of an event
log stored in its entirety. In some scenarios, however, events keep coming with
a high arrival rate to the extent that it is impractical to store the entire
event log and to continuously re-discover a process model from scratch. Such
scenarios require online process discovery approaches. Given an event stream
produced by the execution of a business process, the goal of an online process
discovery method is to maintain a continuously updated model of the process
with a bounded amount of memory while at the same time achieving similar
accuracy as offline methods. However, existing online discovery approaches
require relatively large amounts of memory to achieve levels of accuracy
comparable to that of offline methods. Therefore, this paper proposes an
approach that addresses this limitation by mapping the problem of online
process discovery to that of cache memory management, and applying well-known
cache replacement policies to the problem of online process discovery. The
approach has been implemented in .NET, experimentally integrated with the Minit
process mining tool and comparatively evaluated against an existing baseline
using real-life datasets.
