Business processes in which decisions are made by human resources suffer from noise. Noise is unwanted variability, which leads to inconsistent and unrepeatable decisions and impacts trust negatively. In this paper, we present the Decision Noise Instrument (DNI) to quantitatively estimate the noise that can be attributed to unknown factors affecting the actors who carry out the business process. For our estimation, we solely use readily available data from business information systems that support the execution of the process. In our method, we limit the influence of factors that cannot be attributed to actors. The DNI makes it possible to compare the noise levels between and within business processes. Based on that comparison, further investigation into the problem and noise reduction efforts can be prioritized. We evaluate the DNI on a claim handling process of UWV, a public service provider in the Netherlands. Our results show that we can estimate differences between the noise levels of several decisions within the selected process of UWV. By drilling down on a decision, the subgroups that have the most impact on the noise level are identified.

This contribution revisits our article titled “A General Process Mining Framework for Correlating, Predicting, and Clustering Dynamic Behavior Based on Event Logs”, published in 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.

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.

Discovering good process models is essential for different process analysis tasks such as conformance checking and process improvements. Automated process discovery methods often overlook valuable domain knowledge. This knowledge, including insights from domain experts and detailed process documentation, remains largely untapped during process discovery. This paper leverages Large Language Models (LLMs) to integrate such knowledge directly into process discovery. We use rules derived from LLMs to guide model construction, ensuring alignment with both domain knowledge and actual process executions. By integrating LLMs, we create a bridge between process knowledge expressed in natural language and the discovery of robust process models, advancing process discovery methodologies significantly. To showcase the usability of our framework, we conducted a case study with the UWV employee insurance agency, demonstrating its practical benefits and effectiveness.

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.

Process mining is a discipline that enables organizations to discover and analyze their work processes. A prerequisite for conducting a process mining initiative is the so-called event log, which is not always readily available. In such cases, extracting an event log involves various time-consuming tasks, such as creating tailor-made structured query language (SQL) scripts to extract an event log from a relational database. With this work, we investigate the use of large language models (LLMs) to support event log extraction, particularly by leveraging LLMs ability to produce SQL scripts. In this paper, we report on how effectively an LLM can assist with event log extraction for process mining. Despite the intrinsic non-deterministic nature of LLMs, our results show the potential of future LLM-assisted event log extraction tools, especially when domain and data knowledge are available. The implementation of such tools can increase access to event log extraction to a broader range of users within an organization by reducing the reliance on specialized technical skills for producing relational database query scripts and minimizing manual effort.

Process discovery aims to derive process models from event logs, providing insights into operational behavior and forming a foundation for conformance checking and process improvement. However, models derived solely from event data may not accurately reflect the real process, as event logs are often incomplete or affected by noise, and domain knowledge, an important complementary resource, is typically disregarded. As a result, the discovered models may lack reliability for downstream tasks. We propose an interactive framework that incorporates domain knowledge, expressed in natural language, into the process discovery pipeline using Large Language Models (LLMs). Our approach leverages LLMs to extract declarative rules from textual descriptions provided by domain experts. These rules are used to guide the IMr discovery algorithm, which recursively constructs process models by combining insights from both the event log and the extracted rules, helping to avoid problematic process structures that contradict domain knowledge. The framework coordinates interactions among the LLM, domain experts, and a set of backend services. We present a fully implemented tool that supports this workflow and conduct an extensive evaluation of multiple LLMs and prompt engineering strategies. Our empirical study includes a case study based on a real-life event log with the involvement of domain experts, who assessed the usability and effectiveness of the framework.

Extracted event data from information systems often contain a variety of process executions making the data complex and difficult to comprehend. Unlike current research which only identifies the variability over time, we focus on other dimensions that may play a role in the performance of the process. This research addresses the challenge of effectively segmenting cases within operational processes based on continuous features, such as duration of cases, and evaluated risk score of cases, which are often overlooked in traditional process analysis. We present a novel approach employing a sliding window technique combined with the earth mover's distance to detect changes in control flow behavior over continuous dimensions. This approach enables case segmentation, hierarchical merging of similar segments, and pairwise comparison of them, providing a comprehensive perspective on process behavior. We validate our methodology through a real-life case study in collaboration with UWV, the Dutch employee insurance agency, demonstrating its practical applicability. This research contributes to the field by aiding organizations in improving process efficiency, pinpointing abnormal behaviors, and providing valuable inputs for process comparison, and outcome prediction.

Process discovery aims to discover descriptive process models from event logs. These discovered process models depict the actual execution of a process and serve as a foundational element for conformance checking, performance analyses, and many other applications. While most of the current process discovery algorithms primarily rely on a single event log for model discovery, additional sources of information, such as process documentation and domain experts' knowledge, remain untapped. This valuable information is often overlooked in traditional process discovery approaches. In this paper, we propose a discovery technique incorporating such knowledge in a novel inductive mining approach. This method takes a set of user-defined or discovered rules as input and utilizes them to discover enhanced process models. Our proposed framework has been implemented and tested using several publicly available real-life event logs. Furthermore, to showcase the framework's effectiveness in a practical setting, we conducted a case study in collaboration with UWV, the Dutch employee insurance agency.

Generating anomalies for process executions helps to train anomaly detection methods and evaluate their performance. Anomalous behavior tends to be diverse and very infrequent. Generating process anomalies can help compare detection models and select the suited ones. However, little research has been focused on generating anomalous behavior in a systematic and also stochastic way. In this paper, we built on the idea of training a Markov chain using an event log to capture regular process behavior. We then use a set of predefined anomaly patterns to adapt the Markov chain to generate anomalous traces. To evaluate the quality of our generated anomalies, we use them in the downstream task training a detection model. For each pattern, we vary the quantity of injected anomalous traces and their deviation rate. Unsurprisingly, the results show that the models trained with the generated anomalies have a significant improvement in detecting these anomalies. The AUC score increased from 0.63 to reaching a maximum of 0.98 or higher for all three patterns. This confirms our expectation that generating anomalies can help train and evaluate detection models.

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.

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. This paper discusses monitoring, predicting, and recommending using a PAR system within a financial institute in the Netherlands to avoid faulty executions. While predictions were based on the analysis of historical data, the most opportune intervention was selected on the basis of human judgment and subjective opinions. The results showed that, while the predictions of risky cases were relatively accurate, no reduction was observed in the number of faulty executions. We believe that this was caused by incorrect choices of interventions. While a large body of research exists on monitoring and predicting based on facts recorded in historicaldata, research on fact-based interventions is relatively limited. This paper reports on lessons learned from the case study in finance and proposes a new methodology to improve the performances of PAR systems. This methodology advocates the importance of several cycles of interactions among all actors involved so as to develop interventions that incorporate their feedback and are based on insights from factual, historical data.

Process mining is not only about discovery and conformance checking of business processes. It is also focused on enhancing processes to improve the business performance. While from a business perspective this third main stream is definitely as important as the others if not even more, little research work has been conducted. The existing body of work on process enhancement mainly focuses on ensuring that the process model is adapted to incorporate behavior that is observed in reality. It is less focused on improving the performance of the process. This paper reports on a methodology that creates an enhanced model with an improved performance level. The enhancements of the model limit incorporated behavior to only those parts that do not violate any business rules. Finally, the enhanced model is kept as close to the original model as possible. The practical relevance and feasibility of the methodology is assessed through two case studies. The result shows that the process models improved through our methodology, in comparison with state-of the art techniques, have improved KPI levels while still adhering to the desired prescriptive model.

Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lion׳s share of process mining research has been focusing on process discovery (creating process models from raw data) and replay techniques to check conformance and analyze bottlenecks. These techniques have helped organizations to address compliance and performance problems. However, for a more refined analysis, it is essential to correlate different process characteristics. For example, do deviations from the normative process cause additional delays and costs? Are rejected cases handled differently in the initial phases of the process? What is the influence of a doctor׳s experience on treatment process? These and other questions may involve process characteristics related to different perspectives (control-flow, data-flow, time, organization, cost, compliance, etc.). Specific questions (e.g., predicting the remaining processing time) have been investigated before, but a generic approach was missing thus far. The proposed framework unifies a number of approaches for correlation analysis proposed in literature, proposing a general solution that can perform those analyses and many more. The approach has been implemented in ProM and combines process and data mining techniques. In this paper, we also demonstrate the applicability using a case study conducted with the UWV (Employee Insurance Agency), one of the largest “administrative factories” in The Netherlands.

Today’s Process-Aware Information Systems (PAIS) logs huge amount of data. The data contains a set of activities that are actual executed with in a business process. For example place a request for unemployment claim or pay compensation. This is a starting point for doing the analysis of a business process using Process Mining techniques. In Process Mining, one technique named LogOnMapReplay, which is dynamically visualizing the executed business processes by producing a process movie. This tool is a prototype that’s why it comes along with various limitations and missing functionalities. This report describes the steps taken to overcome the limitations of the tool. It also describes design and implementation of various functionalities developed on LogOnMapReplay tool. This report also describes an evaluation conducted on UWV unemployment business process to find the usefulness and intuitiveness of the tool.

Process discovery techniques make it possible to automatically derive process models from event data. However, often one is not only interested in discovering the control-flow but also in answering questions like "What do the cases that are late have in common?", "What characterizes the workers that skip this check activity?", and "Do people work faster if they have more work?", etc. Such questions can be answered by combining process mining with classification (e.g., decision tree analysis). Several authors have proposed ad-hoc solutions for specific questions, e.g., there is work on predicting the remaining processing time and recommending activities to minimize particular risks. However, as shown in this paper, it is possible to unify these ideas and provide a general framework for deriving and correlating process characteristics. First, we show how the desired process characteristics can be derived and linked to events. Then, we show that we can derive the selected dependent characteristic from a set of independent characteristics for a selected set of events. This can be done for any process characteristic one can think of. The approach is highly generic and implemented as plug-in for the ProM framework. Its applicability is demonstrated by using it to answer to a wide range of questions put forward by the UWV (the Dutch Employee Insurance Agency).