An extended chronicle discovery approach to find temporal patterns between sequences

Abstract

Sequences of events describing the behavior and actions of users or systems can be collected in several domains. An episode is a collection of events that occurs relatively close to each other in a given partial order. Also, chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds and reflect the temporal evolution of the system over the time. In this paper, the problem of finding rules for describing or predicting the behavior of the sequences with the intention of characterizing some interesting tasks is considered. Obtaining these patterns is the main objective of this work, where an automatic method to learn relevant and discriminating chronicles is proposed. The method extends existing algorithms that have been proposed to find frequent episodes/chronicles in a single event sequence to the case of multiple sequences.