Classification of Petri nets models. Synchronic distances and facts.. Petri nets languages. Marked graphs and Free choices Petri nets, Petri nets with inhibitors. Coloured and hierarchical Petri nets. Theoretical and practical background for application of Petri nets and supporting tools in system modelling, design, and verification.
Abilities to apply and develop advanced information technologies based on suitable formal models, to propose and use such models and theories for automating the design, implementation, and verification of computer-based systems.
Theory and Applications of Petri Nets
Reisich W. CERM, Brno Assesment methods and criteria linked to learning outcomes. Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is Syllabus of lectures: Introduction to Petri nets, basic notions.
Analysis of Coloured Petri nets. Hierarchical Coloured Petri nets and Object oriented Petri nets. The following, is the depicts a subnet layer Treatment sub-module of the main model. It models operation in the examination room of the health centre under study. From our data, entry of each patient to the health centre is modelled by a token on the place next patient.
This place has the color set UNIT and the color set UNIT is defined to be equal to unit timed type as depicted in the declarations block of the developed model. From Fig. The place waiting patients has the color set Patients defined to be set of list patient.
Advances in Petri Net
The color set patients is used to model the queue of patients to be attended to. The color set patient models a patient as a record consisting of two fields. The first field denoted with PatientType is of type PatientType and represents type of the patient. Second field is denoted with the title AT is of type real and represents arrival time of a patient. This distribution is used instead of lognormal distribution because currently CPN tools Version 4. The place waiting patients and the place pwfdasse are used to model the queue of patients at the registration counter unit and examination room respectively.
The single token on each of the places waiting patients and the place pwfdasse represents the queue of patients. In the initial marking the lists are empty.
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The places free and busy are used to represent the status of the medical attendant. A token on the place free indicates that the medical attendant is not serving a patient at that time. A token on the place busy indicates that the medical attendant is busy attending to a patient and the value of the token indicates which patient is being processed.
The initial marking of busy is empty. The medical attendant can start attending to patient transition start service , if the medical attendant is free and if there is at least one patient in the queue of patients patient::patients on the arc from place waiting patients to transition start service. Here, compare with several research on the basis of mining loops, hidden tasks, delta analysis, visualizing, process rediscovery, duplication tasks, noise and concurrent processes Table 4. More recently, to deal with less structured, i.
Since this method is also pertinent to the area of mining the activity logs, in the future, it should also compare it to the existing approaches in this area. Agrawal, R. Gunopulos and F. Leymann, Mining process models from workflow logs. Akkermans, H. Gordijn Eds.
PETRI NETS & ACSD - Kanazawa, Japan
Springer, New York, pp: Amyot, D. Horkoff, D. Gross and G. Mussbacher, Pernul Eds. Arbaoui, S. Oquendo, Goal Oriented VS. Ashkan, A. Clarke, E. Agichtein and Q. Guo, Classifying and Characterizing Query Intent. Berrut, J. Mothe and C.
Soule-Dupuy Eds. Assar, S.
Achour and S. Si-Said, Un modele pour la specification des processus d'analyse des systemes d'Information. Baeza-Yates, R. Applications of Web Query Mining. Fernandez-Luna Eds. Calderon-Benavides and C.
Gonzalez-Caro, The Intention Behind Web Queries. Ferragina and M. Sanderson Eds. Barrios, J. Nurcan, Stirna Eds. Bass, L. Clements and R. Kazman, Software Architecture in Practice.
Baum, L. Petrie, Statistical inference for probabilistic functions of finite state Markov chains. Petrie, G. Soules and N. Weiss, A maximization technique occurring in the statistical analysis of probabilistic functions of Markov Chains.