Software Engineering with Petri Nets: A Web Service and Agent Perspective

Betz, Tobias; Cabac, Lawrence; Duvigneau, Michael; Wagner, Thomas; Wester-Ebbinghaus, Matthias

doi:10.1007/978-3-662-45730-6_3

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…On the other hand, CPNs have also been used in software development, in some cases to simulate information sending and processing processes (BETZ et al, 2014). For example, Figure 11 shows an example of simulation screens carried out with CPN Tools technology, of a device to detect student fingerprints to take their respective attendance, which was modeled in CPN and presented in (OJO et al, 2018).…”

Section: Application In Software Developmentmentioning

confidence: 99%

Petri Net Applications in Systems

Rueda

2023

JASCS

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Petri nets are a tool that has been booming mainly in flexible manufacturing engineering and its processes, to perform modeling, control and diagnosis of systems. This work presents a description of Petri nets with their mathematical and graphic representation to introduce the tool and facilitate its understanding, and then establish a general overview of its applications as a result of the respective bibliographic review. It also provides examples as part of the results of its application. Finally, it is concluded that it can be incorporated into various areas where complex systems or processes are used, and even as part of a teaching tool.

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Section: Application In Software Developmentmentioning

confidence: 99%

Petri Net Applications in Systems

Rueda

2023

JASCS

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“…Thirdly, according to Ebbinghaus forgetting curve, users' interest changes over time [5]. Therefore it is difficult to model the user preference.…”

Section: Introductionmentioning

confidence: 99%

Offline Shop Recommendation based On Online Shopping History

Mo¹,

Chen²

2015

Proceedings of the 2015 International Conference on Education Technology, Management and Humanities Science

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Most existing POI recommender systems is facing the problem of sparsely of data. In this paper, an O2O (Online to Offline) recommendation method is proposed, which can take advantage of users' online shopping history data. User behavior is converted to user preference, modeled as user and interest-term matrix. Based on the model, the result list of offline shop is recommended by O2O recommendation algorithm. The algorithm is implemented, and experiments on the real dataset show the feasibility of the method.

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