The Impact of Event Log Subset Selection on the Performance of Process Discovery Algorithms
Abstract
Process discovery algorithms automatically discover process models on the basis of event data, captured during the execution of business processes. These algorithms tend to use all of the event data to discover a process model. When dealing with large event logs, it is no longer feasible using standard hardware in limited time. A straightforward approach to overcome this problem is to down-size the event data by means of sampling. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms. Our experiments show that it is possible to speed up discovery considerably using ranking-based strategies. Furthermore, results show that biased selection of the process instances compared to random selection of them will result in process models with higher quality.
