Abstract A novel and efficient method for discovering concurrent workflow processes is presented. It allows building a suitable workflow net (WFN) from a large event log $$\lambda $$ λ , which represents the behaviour of complex iterative processes involving concurrency. First, thet-invariants are determined from $$\lambda $$ λ ; this allows computing the causal and concurrent relations between the events and the implicit causal relations between events that do not appear consecutively in $$\lambda $$ λ . Then a 1-bounded WFN is built, which could be eventually adjusted if itst-invariants do not match with those computed from $$\lambda $$ λ . The discovered model allows firing all the traces in $$\lambda $$ λ . The procedures derived from the method are polynomial time on $$|\lambda |$$ |λ| ; they have been implemented and tested on artificial logs.

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.

In this paper the problem of discovering a Petri net (PN) from sampled events sequences representing the execution of industrial or business processes is addressed A method for building a 1-bounded PN from a single event sequence S composed of numerous execution traces is presented; it is based on determining causal and concurrency relations between tasks. A technique for computing the t-invariants of the PN from S is proposed; the obtained invariants allow determining the structure of a PN that executes S. The algorithms derived from the method have been implemented and tested on numerous examples of diverse complexity.