Incremental Discovery of Process Maps
Process mining is a body of methods to analyze event logs produced during the execution of business processes in order to extract insights for their improvement. A family of process mining methods, known as automated process discovery, allows analysts to extract business process models from event logs. Traditional automated process discovery methods are intended to be used in an offline setting, meaning that the process model is extracted 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 automated process discovery approaches. Given an event stream produced by the execution of a business process, the goal of an online automated 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. Existing automated discovery approaches require relatively large amounts of memory to achieve levels of accuracy comparable to that of offline methods. This thesis proposes a online process discovery framework 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 proposed framework has been implemented in .NET, integrated with the Minit process mining tool and comparatively evaluated against an existing baseline, using real-life datasets.
Graduation Thesis language
Graduation Thesis type
Master - Software Engineering
Marlon Dumas, Michal Rosik