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.