Checking marking reachability with the state equation in Petri net subclasses

With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph, which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next, the bidirectional recurrent neural network with attention is applied to each transition partition to encode the prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of a Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.

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“…Definition 3 (Prefix, suffix) [23] is given, be a positive integer. The event prefix and suffix of length k can be denoted as: and .…”

Section: Partition Using the Transitions Of Reachability Graphmentioning

confidence: 99%

Explainable Business Process Remaining Time Prediction Using Reachability Graph

Cao

Zeng

et al. 2023

Chinese J. Elect.

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“…Besides these two main kinds of reduction rules (redundancy and agglomeration), the Reduce tool can also identify other opportunities for reductions. For instance specific structural or behavioural restrictions, such as nets that are marked graphs or other cases where the set of reachable markings is exactly defined by the solutions of the state equation [25].…”

Section: Deriving E-equivalences Using Reductionsmentioning

confidence: 99%

On the Combination of Polyhedral Abstraction and SMT-Based Model Checking for Petri Nets

Amat

Berthomieu

Zilio

2021

Application and Theory of Petri Nets and Concurrency

Self Cite

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We define a method for taking advantage of net reductions in combination with a SMT-based model checker. We prove the correctness of this method using a new notion of equivalence between nets that we call polyhedral abstraction. Our approach has been implemented in a tool, named SMPT, that provides two main procedures: Bounded Model Checking (BMC) and Property Directed Reachability (PDR). Each procedure has been adapted in order to use reductions and to work with arbitrary Petri nets. We tested SMPT on a large collection of queries used during the 2020 edition of the Model Checking Contest. Our experimental results show that our approach works well, even when we only have a moderate amount of reductions.

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