  Process pattern discovery methods (PPDMs) aim at identifying patterns of
interest to users. Existing PPDMs typically are unsupervised and focus on a
single dimension of interest, such as discovering frequent patterns. We present
an interactive multi interest driven framework for process pattern discovery
aimed at identifying patterns that are optimal according to a multi-dimensional
analysis goal. The proposed approach is iterative and interactive, thus taking
experts knowledge into account during the discovery process. The paper focuses
on a concrete analysis goal, i.e., deriving process patterns that affect the
process outcome. We evaluate the approach on real world event logs in both
interactive and fully automated settings. The approach extracted meaningful
patterns validated by expert knowledge in the interactive setting. Patterns
extracted in the automated settings consistently led to prediction performance
comparable to or better than patterns derived considering single interest
dimensions without requiring user defined thresholds.
