Predictive Process Monitoring (PPM) of collaborative, inter-organizational processes requires reasoning over multiple interdependent entities, including participants, messages, local executions, and the global collaboration case. Existing approaches extend traditional event logs with collaboration attributes but retain a single-case perspective, leaving much of this structure implicit. Object-centric process mining (OCPM) provides an alternative by representing these entities as first-class objects with explicit relations and multiple notions of case. This study connects collaborative PPM and OCPM through three contributions: (i) a formal semantic mapping from extended collaborative event logs to an OCED-conformant object-centric representation, serialized in OCEL 2.0; (ii) a reformulation of collaborative prediction tasks as object-centric prediction tasks; and (iii) a reproducible converter and prediction pipeline implementing the proposed mapping. We evaluate the framework on four public collaborative event logs and a fifth derived from the BPI Challenge 2013 incident-management log by executing the fourteen reformulated tasks using five predictive strategies across tabular, sequential, and graph-native encodings. We further discuss the benefits, limitations, and threats to the approach's validity. The representation makes collaboration structure explicit and makes it natural to state prediction targets based on object relations that fall outside the case-centric taxonomy, at the cost of increased relational complexity and dependence on object-centric tooling.

Robotic Process Automation (RPA) tools have recently incorporated autonomous LLM-based agents to automate complex human-interaction tasks without disrupting internal systems. Its use within Business Process Management Systems (BPMS) enables process optimization, error minimization, and reduced time commitment. This study presents a practical evaluation of UiPath, Microsoft Power Automate, and Automation Anywhere, and their integration with Camunda BPMS through three automation scenarios. The results indicate that all tools contribute to increasing autonomous capacity and improving operational processes, with strengths in different characteristics.

Robotic process automation (RPA) technologies enable the construction, execution, and management of “robots” that emulate human actions within an information system. Given the significant diversity of available RPA tools, selecting one can be a challenging task for organizations. A systematic approach and detailed evaluation, contextualized to the organizational environment in which the tool will be used, can significantly improve the selection process. In this paper, we adapt and apply an evaluation methodology for RPA tools derived from an existing methodology for evaluating Business Process Management Systems. The methodology proposes the selection and ranking of a set of characteristics of interest, supports exploratory screening, and facilitates both theoretical and practical assessment, thereby accommodating various evaluation strategies tailored to organizational needs. Its practical application is illustrated through a real-world study, in which we performed an exploratory analysis on an initial list of 30 commercial RPA tools, and then a theoretical evaluation of the two most suitable for the client’s environment. Results demonstrate that the methodology can rapidly focus attention on the most promising RPA tools.

Process Mining (PM) provides techniques for analyzing event logs to support evidence-based improvement and process discovery. However, multi-organizational collaborative processes remain difficult to design and analyze due to their distributed nature and technological diversity. Recent advances in Large Language Models and Robotic Process Automation offer new opportunities to support process discovery, modeling, and automation. In parallel, Green BPM emphasizes the need to incorporate sustainability considerations into process design and execution. This project aims to define and evaluate techniques, algorithms, methodologies, and strategies for integrating PM with RPA and Generative AI, considering sustainability elements for the design, automation, and analysis of hyper-connected collaborative processes for e-Government, leveraging real data from the Uruguayan Digital Government Agency (AGESIC), linking theoretical and practical results.

Educational Process Mining (EPM) is a data analysis technique that is used to improve educational processes. It is based on Process Mining (PM), which involves gathering records (logs) of events to discover process models and analyze the data from a process-centric perspective. One specific application of EPM is curriculum mining, which focuses on understanding the learning program students follow to achieve educational goals. This is important for institutional curriculum decision-making and quality improvement. Therefore, academic institutions can benefit from organizing the existing techniques, capabilities, and limitations. We conducted a systematic literature review to identify works on applying PM to curricular analysis and provide insights for further research. We reviewed 27 primary studies published across seven major databases. Our analysis classified these studies into five main research objectives: discovery of educational trajectories, identification of deviations in student behavior, bottleneck analysis, dropout / stopout analysis, and generation of recommendations. Key findings highlight challenges such as standardization to facilitate cross-university analysis, better integration of process and data mining techniques, and improved tools for educational stakeholders. This review provides a comprehensive overview of the current landscape in curricular process mining and outlines specific research opportunities to support more robust and actionable curricular analyses in educational settings.

Predictive Process Monitoring (PPM), a subfield of Process Mining, leverages historical execution data and real-time observations to predict the future states or outcomes of ongoing business process instances. Existing techniques enable predictions, such as the next process activity or the completion time. These capabilities support proactive management by identifying potential deviations, violations, and delays, allowing organizations to implement preventive measures, such as reallocating resources. However, Process Mining research has predominantly focused on orchestration-type processes executed within a single organization (intra-organizational processes). Collaborative (inter-organizational) processes span multiple participant organizations and introduce additional complexity and challenges for their execution and analysis. Despite their significance, PPM for collaborative processes has received limited attention. This study addresses this gap by investigating PPM for collaborative processes. We identify predictions of particular relevance in collaborative environments, such as forecasting the following message exchange between participants. Furthermore, we propose adapting traditional PPM techniques designed for orchestration-type processes to suit the unique requirements of collaborative processes. This adaptation is implemented by developing a tool, Predict-Collab, extending the ProcessTransformer, which we preliminarily assess to demonstrate its potential.

Inter-organizational collaborative business processes involve interactions between participants from different organizations to achieve a common objective collaboratively. From a modeling perspective, there are two complementary views of interest: the choreography, which focuses on the message exchanged between participants, and the collaboration, which also describes the orchestration of each participant’s activities. Traditional process discovery techniques do not consider participants or interactions between them since they usually focus on processes within a single participant. The discovery of inter-organizational collaborative business processes poses many challenges due to data scattered across participants with diverse infrastructures and technologies. This paper presents an approach for discovering inter-organizational collaborative business processes and tool support named Collaborative Miner. Based on an extended XES event log containing participants and message information, it uses existing discovery algorithms to uncover BPMN 2.0 collaboration process models and choreographies. The approach is evaluated using collaborative business process data from selected related work, with results discussed and compared across different scenarios.

Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration type, expand several organizations (for example, in e-Government), adding complexity and various challenges both for their implementation and for their discovery, prediction, and analysis of their execution. Predictive process monitoring is based on exploiting execution data from past instances to predict the execution of current cases. It is possible to make predictions on the next activity and remaining time, among others, to anticipate possible deviations, violations, and delays in the processes to take preventive measures (e.g., re-allocation of resources). In this work, we propose an extension for collaborative processes of traditional process prediction, considering particularities of this type of process, which add information of interest in this context, for example, the next activity of which participant or the following message to be exchanged between two participants.

Inter-organizational collaborative business processes involve at least two participants from different organizations who interact with each other. Discovering and analyzing this kind of process adds several challenges to process mining initiatives, mainly due to traces spread across organizations with heterogeneous infrastructures, technologies, and data models. This paper presents a process mining approach for discovering inter-organizational collaborative business processes, which takes as input an extended XES event log with information on participants and messages exchanged to discover a BPMN 2.0 collaboration process model and corresponding choreography. We evaluated our approach using collaborative business process data from selected related work, discussing and comparing our results within different scenarios.

Object-centric process mining has been proposed as a practical solution for dealing with the multiple views from which a process can be analyzed regarding its relation with organizational data (i.e., objects). The Object-Centric Event Data (OCED) metamodel was recently proposed as a data exchange standard for object-centric process mining. As far as we know, the metamodel has yet to be studied from a Model-Driven Engineering (MDE) perspective. This paper provides an Ecore-based representation of the OCED metamodel and explores its capabilities from an MDE perspective. We also study how the Business Process and Organizational Data Integrated Metamodel (BPODIM), i.e., a proposal for integrating process and organizational data, can be aligned with OCED providing fruitful information for OCED improvement.

Process mining is shifting towards use cases that explicitly leverage the relations between data objects and events under the term of object-centric process mining. Realizing this shift and generally simplifying the exchange and transformation of data between source systems and process mining solutions requires a standardized data format for such object-centric event data (OCED). This report summarizes the activities and results for identifying requirements and challenges for a community-supported standard for OCED. (1) We present a proposal for a core model for object-centric event data that underlies all known use cases. (2) We detail the limitations of the core model wrt. a broad range of use cases and discuss how to overcome them through conventions, usage patterns, and extensions of OCED, exhausting the design-space for an OCED data model and the inherent trade-offs in representing object-centric event data. (3) These insights are backed by five independent OCED implementations which are presented alongside a series of lessons learned in academic and industrial case studies. The results of this report provide guidance to the community to start adopting and building new process mining use cases and solutions around the reliable concepts for object-centric event data, and to engage in a structured process for standardizing OCED based on the known OCED design space.

A process family represents a set of processes with common aspects and variable parts based on an organization's specific business requirements. It can be expressed using concrete modeling languages and then customized at design time into a specific process variant to enact according to particular configuration requirements. Most process family modeling proposals focus on imperative processes, such as extensions of the Business Process Model and Notation (BPMN) standard. Some proposals also focus on declarative languages that express more flexible process families. However, no proposal focuses on the Case Management Model and Notation (CMMN) standard, a declarative proposal that complements BPMN for expressing hybrid processes. In this article, we study declarative process family languages, and we introduce CMMNext, an approach for its modeling and automatic customization based on CMMN. We also present a supporting tool and compare CMMNext's potential concerning other declarative proposals.

Cybersecurity intelligence involves gathering and analyzing data to understand cyber adversaries’ capabilities, intentions, and behaviors to establish adequate security measures. The MITRE ATT&CK framework is valuable for gaining insight into cyber threats since it details attacker tactics, techniques, and procedures. However, to fully understand an attacker’s behavior, it is necessary to connect individual tactics. In this context, Process Mining (PM) can be used to analyze runtime events from information systems, thereby discovering causal relations between those events. This article presents a novel approach combining Process Mining with the MITRE ATT&CK framework to discover process models of different attack strategies. Our approach involves mapping low-level system events to corresponding event labels from the MITRE ATT&CK taxonomy, increasing the abstraction level for attacker profiling. We demonstrate the effectiveness of our approach using real datasets of human and automated (malware) behavior. This exploration helps to develop more efficient and adaptable security strategies to combat current cyber threats and provides valuable guidelines for future research.

Sustainability has captured the attention of the classical management of business processes. Organizations have become increasingly aware of the need to achieve information technology (IT)-enabled business processes that are successful in their economy and ecological and social impact. In this context, Green BPM concerns business processes' modeling, deployment, optimization, and management with dedicated consideration for environmental consequences. Automated process discovery is a crucial process mining task to help organizations to get knowledge of the process they carry out in their daily operation, providing the basis for insights and evidence-based improvement decisions. Several process discovery algorithms have been developed and evaluated by the classical measures on resulting models, such as fitness, precision, f-score, soundness, complexity (size, structuredness, and control-flow complexity), generalization, and the execution time of the algorithm. Within the context of automated process discovery, sustainability adds a new indicator: energy efficiency. This paper extends a well-known benchmark for evaluating automated process discovery methods, measuring the energy efficiency of selected discovery methods with the same publicly available dataset. The expected contribution is to raise more awareness among the developers of process discovery methods about the energy impact of their solutions beyond the more traditional well-known measures.

There are several forces driving software evolution. One is the business process (BP) misalignment, i.e., when the behavior of the information systems supporting the BPs, or its users, is not aligned with the intended behavior of the BPs identified during the requirement engineering phase. Process Mining (PM) is an essential strategy for BP alignment evaluation. Nevertheless, PM initiatives do not usually focus on connecting BP alignment misfits with concrete software requirements for software evolution. This paper provides initial insights into how PM can support software evolution actions by considering research questions posed during a PM initiative. We exemplify this idea by analyzing administrative procedures within an Electronic Document Management System, providing action guides for its evolution obtained from the PM initiative.

Computer Science curricula usually develop software modeling, design, and implementation skills involving standard languages, best practices, and different languages and architectures. However, they do not often involve the specific modeling, design, and implementation of business processes (BPs) using Business Process Management Systems, which require making BPs explicit (e.g., specified in BPMN 2.0) and driving the system development and execution by such models. A specific theoretical, conceptual, and technological background is needed in this context. This paper presents a project-based, hands-on approach to modeling and developing process-driven systems, which we integrated into our Computer Science curricula. We present the experience and highlight lessons learned about essential elements for students learning.

Understanding attackers’ behavior is crucial to respond to cyberattacks effectively. Process Mining (PM) is a valuable tool that analyzes runtime events from information systems to discover coordinated tasks to achieve an objective. Our previous research explored using PM to profile attackers, specifically in analyzing low-level system processes’ event logs to uncover automated attackers’ behavior. In this article, we propose a method that combines PM with the malicious actor behavior taxonomies of the MITRE ATT&CK Framework to discover process models of observed attack strategies. These taxonomies raise the level of abstraction for attacker profiling. We demonstrate this method using a real dataset focused on human behavior, which provides valuable guidelines for future work and enables the development of more effective and adaptable security strategies to combat current cyber threats.

Urban mobility presents various challenges to favor urban development. These challenges have been traditionally analyzed using transport network optimization and simulation techniques. Nevertheless, it is possible to think of process mining as a complementary approach allowing, among other things, to discover behavioral transportation models, obtain execution measures and detect bottlenecks. The objective of this article is to analyze how suitable PM is for the analysis of urban mobility problems. We use open data from the Metropolitan Transportation System (STM) of Montevideo, Uruguay, which, among other things, provides the ability to record up-to-date information on its transportation network and trips of its citizens. We apply process mining to process discovery, both from buses and their users, and carry out various analyses linking such data with time information, costs, types of users, and city areas.

Business processes can have variants depending on specific business requirements, which lead to the definition of a so-called business process family . Since conventional business process modeling languages, e.g., the Business Process Model and Notation (BPMN), do not explicitly support variants’ specification, several proposals have emerged to deal with it. However, they mainly focus on languages’ definition, while less emphasis is made on providing complete variability management. This article presents a Model-Driven Engineering approach for managing BPMN-based business process families composed of a metamodel for conceptualizing process families, a high-level process for managing them (involving model transformations for the configuration of variants), and tool support for the complete approach. We validated the proposal using a real-world example from a university and an empirical study with real users. Users rated the support tool’s principal functional suitability and usability features as very good. Many improvement opportunities were detected, e.g., version control, collaborative work, and error reporting. We also provide a literature review and thorough evaluation of BPMN-based business process families’ proposals using the VIVACE framework.