Large Language Models (LLMs) have the potential to support the transformation of natural language legal text into a regulatory model, a task conventionally known to be time consuming and error prone when done manually. In this paper, we introduce CLERK: a C ompanion L LM E xpert for modeling R egulatory K nowledge existing in natural language legal texts. CLERK captures regulatory knowledge in the format of Legal Goal Requirements Language (GRL) models. CLERK offers three key contributions, utilizing established prompting techniques: (1) Adopting the Tree-of-Thought (ToT) prompting framework, CLERK streamlines the regulatory modeling process by breaking down complex steps into manageable tasks and focusing on those essential for constructing a Legal GRL model only. (2) The ToT framework enables self-evaluation of intermediate outputs. (3) CLERK enhances consistency and clarity, by leveraging additional in-context learning prompting techniques, such as few-shot prompting and output formatting with an explicit syntax definition. Experiments with eight regulatory articles from two domains (healthcare and energy communities) display a notable improvement brought about by CLERK compared to previous approaches. This improvement pertains to identifying relevant actors, goals and their deontic modalities, as well as the relationships among goals.

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.

Instance spanning constraints (ISC) are the instrument to establish controls across multiple instances of one or several processes. A multitude of applications crave for ISC support. Consider, for example, the bundling and unbundling of cargo across several instances of a logistics process or dependencies between examinations in different medical treatment processes. Non-compliance with ISC can lead to severe consequences and penalties, e.g., dangerous effects due to undesired drug interactions. ISC might stem from regulatory documents, extracted by domain experts. Another source for ISC are process execution logs. Process execution logs store execution information for process instances, and hence, inherently, the effects of ISC. Discovering ISC from process execution logs can support ISC design and implementation (if the ISC was not known beforehand) and the validation of the ISC during its life time. This work contributes a categorization of ISC as well as four discovery algorithms for ISC candidates from process execution logs. The discovered ISC candidates are put into context of the associated processes and can be further validated with domain experts. The algorithms are prototypically implemented and evaluated based on artificial and real-world process execution logs. The results facilitate ISC design as well as validation and hence contribute to a digitalized ISC and compliance management.