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.

Effective resource allocation is crucial for optimizing business processes. Yet, most existing methods focus solely on single-process optimization, overlooking the interdependencies present in multi-process environments. This limitation results in inefficient resource allocation, and scalability challenges. To address this gap, we propose MuProMAC (Multi-Process Multi-Agent Coordination), a novel reinforcement learning-based method designed to optimize resource allocation across multiple interdependent business processes. Unlike prior methods, MuProMAC is the first online resource allocation method that explicitly models the interdependencies between processes and dynamically balances competing resource demands to minimize global average cycle time. We evaluate our method in five multi-process scenarios with different levels of resource contention, comparing it against state-of-the-art online resource allocation methods and three simple baselines. Our results show that MuProMAC is consistently among the top-performing methods in shared-resource environments. It achieves low cycle times and stable performance across different workload conditions, outperforming existing methods through its strong adaptability to evolving business processes and increasing complexity.

An important part of healthcare decision making is to understand how certain actions relate to desired and undesired outcomes. One key challenge is to deal with confounding variables, i.e., variables that influence the relation between actions and outcomes. Existing techniques aim to uncover the underlying statistical relations between actions and outcomes, but either do not account for confounding variables or only consider the process or case level instead of the event level. Therefore, this paper proposes a novel relation mining approach for healthcare processes that 1) explicitly accounts for confounding variables at the event level, and 2) transparently communicates the effect of the confounding variables to the user. We demonstrate the applicability and importance of our approach using two evaluation experiments. We use a real-world healthcare dataset to show that the identified relations indeed provide important input for decision making in healthcare processes. We use a synthetic dataset to illustrate the importance of our approach in the general setting of causal model estimation.

Process mining techniques are valuable to gain insights into and help improve (work) processes. Many of these techniques focus on the sequential order in which activities are performed. Few of these techniques consider the statistical relations within processes. In particular, existing techniques do not allow insights into how responses to an event (action) result in desired or undesired outcomes (effects). We propose and formalize the ARE miner, a novel technique that allows us to analyze and understand these action-response-effect patterns. We take a statistical approach to uncover potential dependency relations in these patterns. The goal of this research is to generate processes that are: (1) appropriately represented, and (2) effectively filtered to show meaningful relations. We evaluate the ARE miner in two ways. First, we use an artificial data set to demonstrate the effectiveness of the ARE miner compared to two traditional process-oriented approaches. Second, we apply the ARE miner to a real-world data set from a Dutch healthcare institution. We show that the ARE miner generates comprehensible representations that lead to informative insights into statistical relations between actions, responses, and effects.

Before the findings of a process mining project can be turned into actionable insights or recommendations, it is essential to make sure that the findings are actually valid. Therefore, the evaluation of the findings is a crucial part of a successful process mining project. Current process mining methodologies, however, fall short in providing actionable support to perform such an evaluation. This is especially true when domain experts are involved. To close this gap, we performed a literature study considering all process mining case studies published in the last two decades. In total, we identified 244 candidate papers of which we analyzed 80 in depth. Based on this literature study, we found a need for a more systematic approach for qualitative evaluations in process mining projects where domain experts are involved. Therefore, we build on these results to propose six validation strategies, which originate from qualitative research. We believe that this proposal for more rigor in the evaluation phase of process mining projects helps to move the discipline forward.

Conformance checking enables organizations to automatically identify compliance violations based on the analysis of observed event data. A crucial requirement for conformance-checking techniques is that observed events can be mapped to normative process models used to specify allowed behavior. Without a mapping, it is not possible to determine if an observed event trace conforms to the specification or not. A considerable problem in this regard is that establishing a mapping between events and process model activities is an inherently uncertain task. Since the use of a particular mapping directly influences the conformance of an event trace to a specification, this uncertainty represents a major issue for conformance checking. To overcome this issue, we introduce a probabilistic conformance-checking technique that can deal with uncertain mappings. Our technique avoids the need to select a single mapping by taking the entire spectrum of possible mappings into account. A quantitative evaluation demonstrates that our technique can be applied on a considerable number of real-world processes where existing conformance-checking techniques fail.

Healthcare can be considerably expensive for both patients and insurance companies. In some cases, high costs in healthcare are an indirect outcome of a low quality of care, for example, when treatments have to be repeated. Unfortunately, identifying the factors that lead to such repetitions is a complex and challenging task. In this paper, we focus on the domain of dental healthcare and develop an approach that can predict treatment repetitions in the context of the implant denture therapy process. The challenges associated with predicting treatment repetitions in this setting are considerable. First, hardly any patient undergoes the exact same series of treatments like another. This results in a high degree of variation in the data. Second, only a few patients experience treatment repetitions. This lead to a highly imbalance in the data. To address these challenges, we develop a prediction technique that particularly exploits the process perspective. What is more, we apply so-called resampling methods to deal with the imbalance in the data. Our resulting model is able to predict treatment repetitions with an AUC value of 0.69.