Educational Process Mining (EPM) is widely used to analyze learning processes in technology-enhanced learning environments. However, most EPM approaches remain case-centric, linking each event to a single entity (e.g., student or course), which prevents the analysis of learning situations involving multiple interacting entities, such as group collaboration or shared resource use. Recent developments in Object-Centric Process Mining (OCPM) address this limitation by allowing events to be connected to multiple related objects. Yet, the educational field lacks a practical, methodological framework for extracting structured, object-centric event data from Learning Management Systems (LMSs). Consequently, Object-Centric Educational Process Mining (OC-EPM) remains underdeveloped. This study introduces a framework for extracting multidimensional, object-centric event data from Moodle to support OC-EPM. The framework follows the OCPM² methodology and is implemented in PM4Moodle, an open-source tool that generates OCEL-compliant logs directly from Moodle. A case study in a university course demonstrates, to our knowledge, the first application of OC-EPM in education. The results show how object-centric data enable richer analyses of interactions among students, groups, assignments, and resources, offering insights into learning processes that cannot be captured with case-centric approaches.

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.

Recent advancements in Artificial Intelligence (AI), particularly Large Language Models (LLMs), have enhanced organizations’ ability to reengineer business processes by automating knowledge-intensive tasks. This automation drives digital transformation, often through gradual transitions that improve process efficiency and effectiveness. To fully assess the impact of such automation, a data-driven analysis approach is needed - one that examines how traditional and AI-enhanced process variants coexist during this transition. Object-Centric Process Mining (OCPM) has emerged as a valuable method that enables such analysis, yet real-world case studies are still needed to demonstrate its applicability. This paper presents a case study from the insurance sector, where an LLM was deployed in production to automate the identification of claim parts, a task previously performed manually and identified as a bottleneck for scalability. To evaluate this transformation, we apply OCPM to assess the impact of AI-driven automation on process scalability. Our findings indicate that while LLMs significantly enhance operational capacity, they also introduce new process dynamics that require further refinement. This study also demonstrates the practical application of OCPM in a real-world setting, highlighting its advantages and limitations.

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 depends on Object-Centric Event Data (OCED), which captures relationships between events and object types, representing different perspectives. Unlike traditional process mining techniques, extracting OCED minimizes the need for repeated log extractions when shifting the analytical focus. However, recording these complex relationships increases the complexity of the log extraction process. To address this challenge, this paper proposes a methodology for extracting OCED based on PM², a well-established process mining framework. Our approach introduces a structured framework that guides data analysts and engineers in extracting OCED for process analysis. We validate this framework by applying it in a real-world educational setting, demonstrating its effectiveness in extracting an Object-Centric Event Log (OCEL), which serves as the standard format for recording OCED, from a learning management system and an administrative grading system.

Analyzing process data at varying levels of granularity is important to derive actionable insights and support informed decision-making. Object-Centric Event Data (OCED) enhances process mining by capturing interactions among multiple objects within events, leading to the discovery of more detailed and realistic yet complex process models. The lack of methods to adjust the granularity of the analysis limits users in leveraging the full potential of Object-Centric Process Mining (OCPM). To address this gap, we propose four OnLine Analytical Processing (OLAP) operations: drill-down, roll-up, unfold, and fold, which enable changing the granularity of analysis when working with Object-Centric Event Log (OCEL). These operations allow analysts to seamlessly transition between detailed and aggregated process models, facilitating the discovery of insights that require varying levels of abstraction. We implemented these operations in an open-source Python library, making it available for researchers and practitioners to use in practice. This approach can empower analysts to perform more flexible and comprehensive process exploration, unlocking actionable insights through adaptable granularity adjustments.

Event logs play a fundamental role in enabling data-driven business process analysis. Traditionally, these logs track events related to a single object, known as the case, limiting the scope of analysis. Recent advancements, such as Object-Centric Event Log (OCEL) and Event Knowledge Graph (EKG), capture better how events relate to multiple objects. However, attributes of objects can change over time, which was not initially considered in OCEL or EKG. While OCEL 2.0 has addressed some of these limitations, there remains a research gap concerning how attribute changes should be accommodated in EKG and how OCEL 2.0 logs can be transformed into EKG. This paper fills this gap by introducing Temporal Event Knowledge Graph (tEKG) and defining an algorithm to convert an OCEL 2.0 log to a tEKG.

Process mining has significantly transformed business process management by introducing innovative data-based analysis techniques and empowering organizations to unveil hidden insights previously buried within their recorded data. The analysis is conducted on event logs structured by conceptual models. Traditional models were defined based on only a single case notion, e.g., order or item in the purchase process. This limitation hinders the application of process mining in practice for which new data models are developed, a.k.a, Event Knowledge Graph (EKG) and Object-Centric Event Log (OCEL).While several tools have been developed for OCEL, there is a lack of process mining tooling around the EKG. In addition, there is a lack of comparison about the practical implication of choosing one approach over another. To fill this gap, the contribution of this paper is threefold.First, it defines and implements an algorithm to transform event logs represented as EKG to OCEL. The implementation is used to transform 5 real event logs based on which the approach is evaluated. Second, it compares the performance of analyzing event logs represented in these two models. Third, it compares and reveals similarities and differences in analyzing processes based on event logs represented in these two models.The results highlight ten important findings, including different approaches in calculating directly-follows relations when analyzing filtered event logs in these models and the limitations of OCEL in supporting event lifecycle and inter-log relation analysis.

Today's space of graph database solutions is characterized by two main technology stacks that have evolved separate from one another: on one hand, there are systems that focus on supporting the RDF family of standards; on the other hand, there is the Property Graph category of systems. As a basis for bringing these stacks together and, in particular, to facilitate data exchange between the different types of systems, different direct mappings between the underlying graph data models have been introduced in the literature. While fundamental properties are well-documented for most of these mappings, the same cannot be said about the practical implications of choosing one mapping over another. Our research aims to contribute towards closing this gap. In this paper we report on a preliminary study for which we have selected two direct mappings from (Labeled) Property Graphs to RDF, where one of them uses features of the RDF-star extension to RDF. We compare these mappings in terms of the query performance achieved by two popular commercial RDF stores, GraphDB and Stardog, in which the converted data is imported. While we find that, for both of these systems, none of the mappings is a clear winner in terms of guaranteeing better query performance, we also identify types of queries that are problematic for the systems when using one mapping but not the other.