Root Cause Analysis Using Rule Mining on Object-Centric Event Logs

Lecture notes in business information processing, pp. 57–69

Abstract

Abstract In business processes, the behavior, evolution and interactions of objects influence the outcome of process instances, and thus the value that a business user may assign to them. For example, in an order-to-cash process, a complete and timely delivery of a package is desirable, but depends on what happens to other objects upstream, like production batches. Negative outcomes call for a Root Cause Analysis (RCA) on the process. While many approaches for RCA using process mining exist, none is native to object-centric frameworks and thus suitable for capturing dependencies across object types. This work presents a method for RCA that operates on object-centric event logs (OCELs). Given an OCEL, our method returns a set of association rules on the activity level. These rules associate descriptive patterns over the various object types occurring at events with patterns indicating the process outcome. The patterns are abstracted from the log with the help of a first-order logic based query engine. A case study confirmed that our method can identify problematic interactions across various object types in real-life business processes.

Authors 3

1. Benedikt Knopp corresponding Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

2. Mahsa Pourbafrani Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

3. Wil M. P. van der Aalst Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany