Event data extracted from information systems serves as the foundation for process mining, enabling the extraction of insights and identification of improvements. Process discovery focuses on deriving descriptive process models from event logs, which form the basis for conformance checking, performance analysis, and other applications. Traditional process discovery techniques predominantly rely on event logs, often overlooking supplementary information such as domain knowledge and process rules. These rules, which define relationships between activities, can be obtained through automated techniques like declarative process discovery or provided by domain experts based on process specifications. When used as an additional input alongside event logs, such rules have significant potential to guide process discovery. However, leveraging rules to discover high-quality imperative process models, such as BPMN models and Petri nets, remains an underexplored area in the literature. To address this gap, we propose an enhanced framework, IMr, which integrates discovered or user-defined rules into the process discovery workflow via a novel recursive approach. The IMr framework employs a divide-and-conquer strategy, using rules to guide the selection of process structures at each recursion step in combination with the input event log. We evaluate our approach on several real-world event logs and demonstrate that the discovered models better align with the provided rules without compromising their conformance to the event log. Additionally, we show that high-quality rules can improve model quality across well-known conformance metrics. This work highlights the importance of integrating domain knowledge into process discovery, enhancing the quality, interpretability, and applicability of the resulting process models.

Process mining enables organizations to discover, monitor, and analyze their work processes based on data. A fundamental requirement for initiating a process mining project is the availability of an event log, which is not always readily available. In such cases, extracting an event log typically involves various time-consuming tasks, such as writing custom structured query language (SQL) scripts to extract relevant data into an event log format from a relational database. In this work, we explore the potential of large language models (LLMs) to support event log extraction for process mining by leveraging LLMs’ ability to produce SQL scripts. We evaluate the effectiveness of LLMs in assisting this process and analyze their performance across a range of scenarios. Despite the inherent non-determinism of LLM outputs, our findings highlight the potential of future LLM-assisted tools in automating event log extraction, particularly when provided with the appropriate domain and data knowledge context. The implementation of such tools could democratize access to process mining by reducing the need for specialized technical expertise for producing relational database query scripts and minimizing manual effort.

This contribution revisits our article titled “A General Process Mining Framework for Correlating, Predicting, and Clustering Dynamic Behavior Based on Event Logs” accepted from the Information Systems journal in 2016. It reflects on how the proposed general framework for process mining has grown in relevance with the rise of AI, emphasizing its value as a extensible approach to transforming event data into analytical and predictive insights. It also discusses how the framework relevance and the underlying message remains valid, including for emerging research directions such as prescriptive analytics, causal and/or object-centric process mining.

Abstract Processes often exhibit significant variability, posing challenges for process discovery and insight extraction. While most studies focus on detecting variability over time (e.g., concept drift), control-flow variability can also manifest across other dimensions, such as case durations or performance metrics. Identifying and understanding these changes is vital for uncovering inefficiencies and undesired behaviors. This paper introduces a novel framework that combines control-flow change detection across performance dimensions with explainability, providing insights into where and how control flow evolves. The framework uses a sliding window approach with the earth mover’s distance to detect behavioral shifts. To enhance interpretability, event logs are encoded into a feature space defined by declarative constraints, capturing intuitive control-flow properties. Clustering these features reveals distinct behavioral patterns and their evolution along performance dimensions, linking detected changes to specific process dynamics. We validate the framework using three real-life event logs, including one from the UWV employee insurance agency in the Netherlands, demonstrating its ability to uncover meaningful changes, explain process variability, and support data-driven decision-making. The framework is implemented as an open-source tool for broader applicability.

Business process models can be (re)constructed using event data recorded during the process' execution. Similarly, event data can be used to verify conformance to prescribed behavior and to analyze and improve the underlying processes. However, not all events that are related to a process necessarily relate to its control-flow. Some events occur in the context of the process. In this work, we introduce the concept of context events to deal with these types of events. We show how distinguishing between contextual and control-flow events aids process discovery to obtain less complex process models. We demonstrate how visualizing context events on top of process models helps identify points in the process where context events occur often, aiding understanding. We analyze these benefits using two case studies involving real-life processes and event data.

Process-aware Recommender systems (PAR systems) are information systems that aim to monitor process executions, predict their outcome, and recommend effective interventions to reduce the risk of failure. While a PAR system is composed by monitoring, predictive analytics and prescriptive analytics, the lion's share of attention in the recent years has been on the first two, overlooking the last. It seems that process participants are tacitly assumed to take the “right decision” for the most appropriate corrective actions in case of failure's risks. Unfortunately, the assumption of selecting an effective corrective action is not always met in reality. When selecting an intervention, this is mainly based on human judgment, which naturally relies on subjective process' perceptions, instead of objective facts. Experience has shown that, when a fact-based predictive analytics is followed by subjective prescriptive analytics, the positive effect of good predictions are nullffied by inconclusive corrective actions, yielding no final improvement. This paper discusses a PAR system that features a data-driven prescriptive analytics framework, which puts aside subjective options and focuses on factual data. The effectiveness of the proposed solution is assessed through the process of a reintegration company, showing a potential increase of customers that find a new job.