Abstract
Data flow in business process modeling is created and distributed by the exchange of data moving from one task to another in information systems. Among open issues in workflow modeling is the detection of errors in data flow and control flow. Researchers have recently focused on detecting errors by applying an active help with a concept of Data-Record. However, this method does not support a loop modeling. This chapter presents a novel active help with a Data-Record concept in order to detect data flow anomalies in loop modeling. We the improve the active help approach using suitable rules for loop modeling where a decision node, using a data connection as an input data, replaces the connector Xor-split. The input data of the decision node is returned to the last activity as a feedback when the error message is found. The proposed approach is validated using a deterministic finite state process model which uses a logic Boolean predicate (Yes or No) to specify the routing of an input data. In this chapter, anomalies such as missing data, conflicting data and redundant data are investigated. The verification is triggered when an anomaly is detected, and the system is locked until a correction is performed. The results show that Missing Data anomalies are efficiently handled by the proposed approach when there is a feedback loop. The simulation is carried out using The Drools platform which introduced RuleFlow tool in KIE (Knowledge Is Everything). For the other anomalies, conflicting data and redundant data are also verified by a uppaal tool in model checking.
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Chadli, N., Kabbaj, M.I., Bakkoury, Z. (2020). An Enhanced Adhoc Approach Based on Active Help to Detect Data Flow Anomalies in a Loop of a Business Modeling. In: Elhoseny, M., Hassanien, A. (eds) Emerging Technologies for Connected Internet of Vehicles and Intelligent Transportation System Networks. Studies in Systems, Decision and Control, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-030-22773-9_9
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