BigCarl: Mining frequent subnets from a single large Petri net

TL;DR

This paper introduces BigCarl, a novel method for mining frequent subnets from a large Petri net by transforming it into a net graph and applying incremental pattern growth, demonstrating efficiency and correctness.

Contribution

It presents a new approach for frequent subnet mining from Petri nets using net graph transformation and incremental pattern growth techniques.

Findings

Method is correct and effective on large Petri nets

Complexity remains reasonable for large-scale data

Approach outperforms existing methods in efficiency

Abstract

While there have been lots of work studying frequent subgraph mining, very rare publications have discussed frequent subnet mining from more complicated data structures such as Petri nets. This paper studies frequent subnets mining from a single large Petri net. We follow the idea of transforming a Petri net in net graph form and to mine frequent sub-net graphs to avoid high complexity. Technically, we take a minimal traversal approach to produce a canonical label of the big net graph. We adapted the maximal independent embedding set approach to the net graph representation and proposed an incremental pattern growth (independent embedding set reduction) way for discovering frequent sub-net graphs from the single large net graph, which are finally transformed back to frequent subnets. Extensive performance studies made on a single large Petri net, which contains 10K events, 40K conditions and 30 K arcs, showed that our approach is correct and the complexity is reasonable.