Efficient Verification of Data-Value-Aware Process Models
|Termin||Fr 21. Mai 2021|
|Kurzfassung||Verification methods detect unexpected behavior of business process models before their execution. In many process models, verification depends on data values. A data value is a value in the domain of a data object, e.g., $1000 as the price of a product. However, verification of process models with data values often leads to state-space explosion. This problem is more serious when the domain of data objects is large. The existing works to tackle this problem often abstract the domain of data objects. However, the abstraction may lead to a wrong diagnosis when process elements modify the value of data objects.
In this thesis, we provide a novel approach to enable verification of process models with data values, so-called data-value-aware process models. A distinctive of our approach is to support modification of data values while preserving the verification results. We show the functionality of our approach by conducting the verification of a real-world application: the German 4G spectrum auction model.