Extracting Process-Aware Decision Models from Object-Centric Process Data
Alexandre Goossens, Johannes De Smedt, Jan Vanthienen
TL;DR
This work tackles discovering decision logic from object-centric process data by introducing the Integrated Object-centric Decision Discovery Algorithm (IODDA). IODDA operates on DOCEL logs through a three-step pipeline—pre-processing to identify potential input/output variables, analysis to build correlation-based decision models, and post-processing to consolidate overlapping models—using the ATAOTS shift concept and thresholds such as $\underline{minShift}$, $\underline{minTraceprop}$, and $\underline{minCorr}$ to yield complete DRD-based DMN structures. The authors validate IODDA on two artificial KiP DOCEL logs, demonstrating accurate recovery of decision structure and logic, and mapping decisions to the correct object types and activities, with interpretable decision trees derived from random forests. They also provide public DOCEL log generators and a Python implementation to support reproducibility and further research in object-centric decision mining.
Abstract
Organizations execute decisions within business processes on a daily basis whilst having to take into account multiple stakeholders who might require multiple point of views of the same process. Moreover, the complexity of the information systems running these business processes is generally high as they are linked to databases storing all the relevant data and aspects of the processes. Given the presence of multiple objects within an information system which support the processes in their enactment, decisions are naturally influenced by both these perspectives, logged in object-centric process logs. However, the discovery of such decisions from object-centric process logs is not straightforward as it requires to correctly link the involved objects whilst considering the sequential constraints that business processes impose as well as correctly discovering what a decision actually does. This paper proposes the first object-centric decision-mining algorithm called Integrated Object-centric Decision Discovery Algorithm (IODDA). IODDA is able to discover how a decision is structured as well as how a decision is made. Moreover, IODDA is able to discover which activities and object types are involved in the decision-making process. Next, IODDA is demonstrated with the first artificial knowledge-intensive process logs whose log generators are provided to the research community.