Diagnosing Process Trajectories Under Partially Known Behavior

Abstract

Diagnosis of process executions is an important task in many application domains, especially in the area of workflow management systems and orchestrated Web Services. If executions fail because activities of the process do not behave as intended, recovery procedures re-execute some activities to recover from the failure. We present a diagnosis method for identifying incorrect activities in process executions. Our method is novel both in that it does not require exact behavioral models for the activities and that its accuracy improves upon dependency-based methods. Observations obtained from partial executions and re executions of a process are exploited. We formally characterize the diagnosis problem and develop a symbolic encoding that can be solved using constraint logic programming techniques. Our evaluation demonstrates that the framework yields superior accuracy to dependency-based methods on realistically-sized examples.