PSpan:Mining Frequent Subnets of Petri Nets
Ruqian Lu, Shuhan Zhang
TL;DR
This paper introduces PSpan, an algorithm for mining frequent complete subnets in Petri nets using a pattern growth approach, applicable to various Petri net subclasses, confirmed by experiments.
Contribution
The paper presents the first algorithm for mining frequent complete subnets in Petri nets, utilizing net graph transformations and pattern growth techniques.
Findings
PSpan effectively mines frequent subnets in Petri nets.
PSpan has similar complexity to gSpan in graph mining.
Experiments confirm PSpan's reliability across Petri net subclasses.
Abstract
This paper proposes for the first time an algorithm PSpan for mining frequent complete subnets from a set of Petri nets. We introduced the concept of complete subnets and the net graph representation. PSpan transforms Petri nets in net graphs and performs sub-net graph mining on them, then transforms the results back to frequent subnets. PSpan follows the pattern growth approach and has similar complexity like gSpan in graph mining. Experiments have been done to confirm PSpan's reliability and complexity. Besides C/E nets, it applies also to a set of other Petri net subclasses.
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Service-Oriented Architecture and Web Services · Petri Nets in System Modeling