Detecting Data-State Anomalies in BPMN 2.0: Unterschied zwischen den Versionen

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(Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Pierre Bonert |email=pierre.bonert@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Jutta Mülle |termin=Institutsseminar/2020-01-…“)
 
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|betreuer=Jutta Mülle
 
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|kurzfassung=Business Process Model and Notation (BPMN) is a standard language to specify business
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|kurzfassung=Business Process Model and Notation (BPMN) is a standard language to specify business process models. It helps organizations around the world to analyze, improve and automate their processes. It is very important to make sure that those models are correct, as faulty models can do more harm than good. While many verification methods for BPMN concentrate only on control flow, the importance of correct data flow is often neglected.
process models. It helps organizations around the world to analyze, improve and automate
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Additionally the few approaches tackling this problem, only do it on a surface level ignoring certain important aspects, such as data states. Because data objects with states can cause different types of errors than data objects without them, ignoring data states can lead to overlooking certain mistakes. This thesis tries to address the problem of detecting data flow errors on the level of data states, while also taking optional data and alternative data into account. We propose a new transformation for BPMN models to Petri Nets and specify suitable anti-patterns. Using a model checker, we are then capable of automatically detecting data flow errors regarding data states. In combination with existing approaches, which detect control flow errors or data flow errors on the level of data values, business process designers will be able to prove with a higher certainty that their models are actually flawless.
their processes. It is very important to make sure that those models are correct, as
 
faulty models can do more harm than good. While many verification methods for BPMN
 
concentrate only on control �ow, the importance of correct data �ow is often neglected.
 
Additionally the few approaches tackling this problem, only do it on a surface level ignoring
 
certain important aspects, such as data states. Because data objects with states
 
can cause different types of errors than data objects without them, ignoring data states
 
can lead to overlooking certain mistakes. This thesis tries to address the problem of detecting
 
data �ow errors on the level of data states, while also taking optional data and
 
alternative data into account. We propose a new transformation for BPMN models to
 
Petri Nets and specify suitable anti-patterns. Using a model checker, we are then capable
 
of automatically detecting data �ow errors regarding data states. In combination with
 
existing approaches, which detect control �ow errors or data �ow errors on the level of
 
data values, business process designers will be able to prove with a higher certainty that
 
their models are actually �flawless.
 
 
}}
 
}}

Version vom 9. Januar 2020, 11:37 Uhr

Vortragende(r) Pierre Bonert
Vortragstyp Bachelorarbeit
Betreuer(in) Jutta Mülle
Termin Fr 17. Januar 2020
Vortragsmodus
Kurzfassung Business Process Model and Notation (BPMN) is a standard language to specify business process models. It helps organizations around the world to analyze, improve and automate their processes. It is very important to make sure that those models are correct, as faulty models can do more harm than good. While many verification methods for BPMN concentrate only on control flow, the importance of correct data flow is often neglected.

Additionally the few approaches tackling this problem, only do it on a surface level ignoring certain important aspects, such as data states. Because data objects with states can cause different types of errors than data objects without them, ignoring data states can lead to overlooking certain mistakes. This thesis tries to address the problem of detecting data flow errors on the level of data states, while also taking optional data and alternative data into account. We propose a new transformation for BPMN models to Petri Nets and specify suitable anti-patterns. Using a model checker, we are then capable of automatically detecting data flow errors regarding data states. In combination with existing approaches, which detect control flow errors or data flow errors on the level of data values, business process designers will be able to prove with a higher certainty that their models are actually flawless.