Incremental Process Discovery using Petri Net Synthesis

Petri net synthesis can avoid the state exploration problem by guaranteeing the correctness in the Petri net while incrementally expanding the net. This paper proposes the conditions imposed on a synthesis shared a kind of subnet under which the following structural properties will be preserved: repetitiveness, consistency, structural boundedness, conservativeness, structural liveness, P-invariant and T-invariant.

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Process Mining and Petri Net Synthesis

Business Process Management Workshops - Lecture Notes in Computer Science ◽

10.1007/11837862_12 ◽

2006 ◽

pp. 105-116 ◽

Cited By ~ 11

Author(s):

Ekkart Kindler ◽

Vladimir Rubin ◽

Wilhelm Schäfer

Keyword(s):

Petri Net ◽

Process Mining ◽

Petri Net Synthesis

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Role of Stochastic Petri Net (SPN) in Process Discovery for Modelling and Analysis

Mathematical Problems in Engineering ◽

10.1155/2021/8699164 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Shabnam Shahzadi ◽

Xianwen Fang ◽

David Anekeya Alilah

Keyword(s):

Petri Net ◽

Business Processes ◽

Time Perspective ◽

Process Mining ◽

Vital Role ◽

Process Discovery ◽

Stochastic Petri Net ◽

Event Log ◽

The Stability ◽

Generalized Stochastic Petri Net

For exploitation and extraction of an event’s data that has vital information which is related to the process from the event log, process mining is used. There are three main basic types of process mining as explained in relation to input and output. These are process discovery, conformance checking, and enhancement. Process discovery is one of the most challenging process mining activities based on the event log. Business processes or system performance plays a vital role in modelling, analysis, and prediction. Recently, a memoryless model such as exponential distribution of the stochastic Petri net SPN has gained much attention in research and industry. This paper uses time perspective for modelling and analysis and uses stochastic Petri net to check the performance, evolution, stability, and reliability of the model. To assess the effect of time delay in firing the transition, stochastic reward net SRN model is used. The model can also be used in checking the reliability of the model, whereas the generalized stochastic Petri net GSPN is used for evaluation and checking the performance of the model. SPN is used to analyze the probability of state transition and the stability from one state to another. However, in process mining, logs are used by linking log sequence with the state and, by this, modelling can be done, and its relation with stability of the model can be established.

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Tissue Systems and Petri Net Synthesis

Lecture Notes in Computer Science - Transactions on Petri Nets and Other Models of Concurrency IX ◽

10.1007/978-3-662-45730-6_7 ◽

2014 ◽

pp. 124-146

Author(s):

Jetty Kleijn ◽

Maciej Koutny ◽

Marta Pietkiewicz-Koutny

Keyword(s):

Petri Net ◽

Tissue Systems ◽

Petri Net Synthesis

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Target-oriented Petri Net Synthesis

A Mosaic of Computational Topics: from Classical to Novel ◽

10.3233/stal200007 ◽

2020 ◽

Author(s):

Eike Best ◽

Raymond Devillers ◽

Evgeny Erofeev ◽

Harro Wimmel

Keyword(s):

Petri Nets ◽

Petri Net ◽

Transition System ◽

Efficient Synthesis ◽

Transition Systems ◽

Np Complete ◽

Full Class ◽

Petri Net Synthesis ◽

Labelled Transition System ◽

Additional Constraints

When a Petri net is synthesised from a labelled transition system, it is frequently desirable that certain additional constraints are fulfilled. For example, in circuit design, one is often interested in constructing safe Petri nets. Targeting such subclasses of Petri nets is not necessarily computationally more efficient than targeting the whole class. For example, targeting safe nets is known to be NP-complete while targeting the full class of place/transition nets is polynomial, in the size of the transition system. In this paper, several classes of Petri nets are examined, and their suitability for being targeted through efficient synthesis from labelled transition systems is studied and assessed. The focus is on choice-free Petri nets and some of their subclasses. It is described how they can be synthesised efficiently from persistent transition systems, summarising and streamlining in tutorial style some of the authors’ and their groups’ work over the past few years.

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