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.

Modern information systems track the behavior of business objects in digital execution records, called objectcentric event logs. An important step in the analysis of these logs is the discovery of a process model that describes the interacting control flows of all objects. In real-life processes, these interactions are often based on the object’s identities. In an order management process, for example, the same set of items that is ordered by a customer is subsequently also delivered. However, existing object-centric modeling formalisms and discovery techniques either ignore these identity relations, treat them as static, or capture them without guaranteeing important behavioral soundness properties. In this paper, we formalize three types of dynamic identity relations that can frequently be found in real-life processes. Then, we introduce a new object-centric modeling formalism that supports these identity relations while guaranteeing important soundness properties by construction. Additionally, we provide a discovery algorithm to construct corresponding process models from object-centric event logs. We prove that the inclusion of dynamic identity relations in our models preserves and improves important model quality criteria. Lastly, we evaluate our approach by applying it to a range of public logs. We observe the discovery of dynamic identity relations in feasible runtime in all investigated logs.

The research area of object-centric process mining provides techniques to model and analyze business processes with interacting object types, such as orders, items and packages. An important step in the analysis of such processes is the automated discovery of a process model that represents the control flow and interaction of all object types. However, existing object-centric discovery algorithms often produce process models that are hard to interpret due to their exccessive complexity. Additionally, they often do not provide formal guarantees on important model properties, such as liveness and identifier-soundness. These properties guarantee, upon executing the model, that all participating objects can properly reach the end of their life-cycle and that all parts of the model can eventually become active. In this paper, we propose a new object-centric discovery algorithm to automatically construct compact object-centric process models that are guaranteed to be live and identifier-sound. For this purpose, we introduce object-centric process trees as an abstract view on object-centric Petri nets that provide both guarantees by construction. We evaluate our approach by applying it to a range of synthetic and real-life logs and find it to be feasible in terms of runtime and unique with regards to its provided guarantees.

Process discovery algorithms extract process models from business process event logs. Existing discovery algorithms require upfront filtering, or specific parameter input, to produce models with balanced quality dimensions on real-life event logs. We propose the Approximate Inductive Miner (AIM) to fill this gap and offer an automated way to discover sound models in polynomial time complexity, without any pre-processing or mandatory parameter input. AIM uses the existing Inductive Miner framework and applies clustering techniques to recursively identify structures in the event log. It additionally performs an approximate parameter optimisation to dynamically suggest a suitable parameter. We compare AIM with existing discovery algorithms on synthetic and real-life event logs, and evaluate the quality of the integrated parameter suggestion. We find that AIM on its own produces sound models with low control flow complexity and high precision, even on complex event logs. Additionally, AIM is able to handle a vast range of event log properties, such as infrequent and incomplete behaviour, without requiring any human parameter input or upfront filtering.