Discovering Petri Net Models of Discrete-Event Processes by Computing T-Invariants.

This paper addresses the problem of discovering a Petri Net (PN) from a long event sequence representing the behavior of discrete-event processes. A method for building a 1-bounded PN able to execute the events sequence $S$ is presented; it is based on determining causality and concurrence relations between events and computing the t-invariants. This novel method determines the structure and the initial marking of an ordinary PN, which reproduces the behavior in $S$. The algorithms derived from the method are efficient and have been implemented and tested on numerous examples of diverse complexity. [ABSTRACT FROM AUTHOR]