Data-Flow Correctness and Compliance Verification for Data-Aware Workflows in Energy Markets

Aus SDQ-Institutsseminar
Version vom 10. November 2017, 13:04 Uhr von Jutta Mülle (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Milena Nedelcheva |email=nedelcheva@outlook.com |vortragstyp=Diplomarbeit |betreuer=Jutta Mülle |termin=Institutsseminar/2017-11-24 |k…“)
(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Vortragende(r) Milena Nedelcheva
Vortragstyp Diplomarbeit
Betreuer(in) Jutta Mülle
Termin Fr 24. November 2017
Vortragsmodus
Kurzfassung - anderer Raum wird noch gesucht -

Data flow is becoming more and more important for business processes over the last few years. Nevertheless, data in work�flows is often considered as second-class object and is not su�fficiently supported. In many domains, such as the energy market, the importance of compliance requirements stemming form legal regulations or specific standards has dramatically increased over the past few years. To be broadly applicable, compliance veri�fication has to support data-aware compliance rules as well as to consider data conditions within a process model. In this thesis we model the data-�flow of data objects for a scenario in the energy market domain. For this purpose we use a scientifi�c work�flow management system, namely the Apache Taverna. We will then insure the correctness of the data �flow of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna work�flows to BPMN 2.0. We then ensure the correctness of the data-�flow of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.