Extracting Process-Aware Decision Models from Object-Centric Process Data
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingObjectSimilar Papers 제목 키워드 기반
OCPM$^2$: Extending the Process Mining Methodology for Object-Centric Event Data Extraction
Object-Centric Process Mining (OCPM) enables business process analysis from multiple perspectives. For example, an educational path can be examined from the viewpoints of students, teachers, and groups. This analysis dep…
ManagementObjectA Framework for Extracting and Encoding Features from Object-Centric Event Data
Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-centric event data contain events associa…
ObjectObject Centric Concept Bottlenecks
Developing high-performing, yet interpretable models remains a critical challenge in modern AI. Concept-based models (CBMs) attempt to address this by extracting human-understandable concepts from a global encoding (e.g.…
Decision MakingObjectPredictive Process Monitoring Using Object-centric Graph Embeddings
Object-centric predictive process monitoring explores and utilizes object-centric event logs to enhance process predictions. The main challenge lies in extracting relevant information and building effective models. In th…
Activity PredictionObject-Centric World Models for Causality-Aware Reinforcement Learning
World models have been developed to support sample-efficient deep reinforcement learning agents. However, it remains challenging for world models to accurately replicate environments that are high-dimensional, non-statio…
Reinforcement Learning