Detecting FNE in Sound Free-choice Petri Net with Data

Nowadays, the development of a third-party service (Express industry) and a third-party payment (Alipay) are very fast in online shopping. Despite there are many technologies to detect control flow errors in business process, the soundness verification in data flow is very hard. To support the design of a workflow, we usually consider the correct control flow structure. However, information about data flow should also be ensured correct. The operation of the system may suffer some external attacks, which makes the task change the read and write operations, which result in changing of control flow structure which would lead to the emergence of unusual system. As a result, our approach provides a new technology to analysis the correctness of sound free-choice Petri net with data (SCDN). With the strong concealment of this attack, the system may suffer false-negative data flow errors (FNE), which would bring some loses to the participants. On the basis of behavioral profiles (BP), redundant data flow errors (RDE) and missing data flow errors (MDE), we provide the theory of FNE to demonstrate the stability, effectiveness and adaptation of our detection methods. Finally, a real E-commerce business system is used to illustrate the practicability of the method provided in this paper.