Discovery of temporal patterns from process instances

Existing work in process mining focuses on the discovery of the underlying process model from their instances. In this paper, we do not assume the existence of a single process model to which all process instances comply, and the goal is to discover a set of frequently occurring temporal patterns. Discovery of temporal patterns can be applied to various application domains to support crucial business decision-making. In this study, we formally defined the temporal pattern discovery problem, and developed and evaluated three different temporal pattern discovery algorithms, namely TP-Graph, TP-Itemset and TP-Sequence. Their relative performances are reported.