Extracting Process-Aware Decision Models from Object-Centric Process Data
This addresses the need for organizations to extract decision insights from complex, multi-object business process data, representing an incremental advancement in process mining.
The paper tackles the problem of discovering decision models from object-centric process logs, which is complex due to multiple objects and sequential constraints, and proposes the first algorithm (IODDA) that can identify decision structures, involved activities, and object types, demonstrated using newly provided artificial logs.
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.