Business process management employs process models and event logs to represent the behavior of the information systems under study. Traditional case-centric notions consider the order of activities and events in isolated process instances. The emerging field of object-centric processes challenges this assumption by putting objects in the center. Object-centric process mining and modeling approaches identify the structure of co-evolving data objects that influence the behavior of an information system to provide a comprehensive view of the system behavior. Object-centricity has been investigated independently in process modeling and in process mining, which resulted in the coexistence of seemingly contradictory assumptions and definitions. As a community effort, this research manifesto relates and aligns existing terminologies, definitions, and perspectives to provide a common ground for current and future research in object-centric business process management. Based on the current state of research, we propose a conceptualization that sets process models and event logs in relation to the information system’s behavior and the execution data it generates. The conceptualization aims at aligning different terminologies and, thus, providing a basis to model and analyze behavioral characteristics. Building on this common ground, we identify open research challenges along the most relevant research areas in object-centric process management. For each research area, its current status is investigated and an outline of the most relevant research challenges is presented.

Abstract Process-aware information systems guide participants through the execution of processes. However, existing systems have limited support for knowledge-intensive processes, which are multi-variant and shaped by informed decisions of knowledge workers. Yet, making such decisions causes high cognitive load, as the effect of the decision on the future process execution must be considered. This may cause errors and/or slow down the process execution. We present an approach based on fragment-based Case Management. It supports the iterative decision making by (i) enabling knowledge workers to define goals and (ii) by giving recommendations on which decision outcomes align with the goals and which do not. For that, we use information from the process model and the running process instance. We show the technical feasibility with a proof-of-concept implementation and the value for knowledge workers in a preliminary user study.