Hybrid Diagnosis Applied to Multiple Instances in Business Processes

Abstract

Business Process compliance is an important issue in control flow and data-flow perspectives. Control-flow correctness can be analysed at design time, whereas data-flow accuracy should be verified at run time, since data is accessed and modified during execution. Compliance validation should consider the conformance of data to business rules. Business compliance rules are policies or statements that govern corpo rate behaviour. Since business compliance rules and data change during process execution, faults can appear due to the erroneous inclusion of rules and/or data in the process. A hybrid diagnosis therefore needs to be performed regarding the likelihood of faults in data vs. business rules. In order to achieve the correct diagnosis, it is fundamental to attain the best assumption concerning the degree of likelihood. In this paper, we present an automatic process to diagnose possible faults that simulta neously combines business rules and data of multiple process instances. This process is based on Constraint Programming paradigm to efficiently ascertain a minimal diagnosis. Furthermore, a methodology for calcula tion of the most appropriate degree of likelihood of faults in data vs. business rules is proposed.