Structural Induction as a Method to Distribute the Generation of a Trace Language Representation for Complex Systems

The paper presents a method to distribute the generation of all possible bounded length sequences of events for a given Petri net. The crucial bene t of our method is that it permits distributing of the generation process and allows to apply Big data processing techniques. The net is decomposed into functional fragments, each of them is simulated independently to produce the set of its sequences, which are combined to yield the sequences for the whole net. This process can be executed by structural induction, and facilitates re-usability of partial results. To limit the amount of data transferred during the computation, which is the critical factor, we represent these sequences in a compact form of Mazurkiewicz traces, which identify all sequences which di er by interchanging the order of subsequent independent events. Therefore a single trace corresponds to all distributed runs of the net, where tasks that can be executed concurrently are recorded in all possible orders. Our tool can be useful in process mining, in particular in conformance checking. In this use case, a Petri net model of a process is compared to its real-life behaviour, recorded in execution logs. As the investigated logs are large, the gain from distributing the computation is desired.