Hajo A. Reijers

Olusanmi Hundogan, Xixi Lu, Yupei Du et al.

Predictive process analytics focuses on predicting future states, such as the outcome of running process instances. These techniques often use machine learning models or deep learning models (such as LSTM) to make such predictions. However, these deep models are complex and difficult for users to understand. Counterfactuals answer ``what-if'' questions, which are used to understand the reasoning behind the predictions. For example, what if instead of emailing customers, customers are being called? Would this alternative lead to a different outcome? Current methods to generate counterfactual sequences either do not take the process behavior into account, leading to generating invalid or infeasible counterfactual process instances, or heavily rely on domain knowledge. In this work, we propose a general framework that uses evolutionary methods to generate counterfactual sequences. Our framework does not require domain knowledge. Instead, we propose to train a Markov model to compute the feasibility of generated counterfactual sequences and adapt three other measures (delta in outcome prediction, similarity, and sparsity) to ensure their overall viability. The evaluation shows that we generate viable counterfactual sequences, outperform baseline methods in viability, and yield similar results when compared to the state-of-the-art method that requires domain knowledge.

Suhwan Lee, Xixi Lu, Hajo A. Reijers

Anomaly detection in process mining focuses on identifying anomalous cases or events in process executions. The resulting diagnostics are used to provide measures to prevent fraudulent behavior, as well as to derive recommendations for improving process compliance and security. Most existing techniques focus on detecting anomalous cases in an offline setting. However, to identify potential anomalies in a timely manner and take immediate countermeasures, it is necessary to detect event-level anomalies online, in real-time. In this paper, we propose to tackle the online event anomaly detection problem using next-activity prediction methods. More specifically, we investigate the use of both ML models (such as RF and XGBoost) and deep models (such as LSTM) to predict the probabilities of next-activities and consider the events predicted unlikely as anomalies. We compare these predictive anomaly detection methods to four classical unsupervised anomaly detection approaches (such as Isolation forest and LOF) in the online setting. Our evaluation shows that the proposed method using ML models tends to outperform the one using a deep model, while both methods outperform the classical unsupervised approaches in detecting anomalous events.

Suhwan Lee, Marco Comuzzi, Xixi Lu et al.

Predictive Process Monitoring aims to forecast the future progress of process instances using historical event data. As predictive process monitoring is increasingly applied in online settings to enable timely interventions, evaluating the performance of the underlying models becomes crucial for ensuring their consistency and reliability over time. This is especially important in high risk business scenarios where incorrect predictions may have severe consequences. However, predictive models are currently usually evaluated using a single, aggregated value or a time-series visualization, which makes it challenging to assess their performance and, specifically, their stability over time. This paper proposes an evaluation framework for assessing the stability of models for online predictive process monitoring. The framework introduces four performance meta-measures: the frequency of significant performance drops, the magnitude of such drops, the recovery rate, and the volatility of performance. To validate this framework, we applied it to two artificial and two real-world event logs. The results demonstrate that these meta-measures facilitate the comparison and selection of predictive models for different risk-taking scenarios. Such insights are of particular value to enhance decision-making in dynamic business environments.

Xixi Lu, Avigdor Gal, Hajo A. Reijers

Processes, such as patient pathways, can be very complex, comprising of hundreds of activities and dozens of interleaved subprocesses. While existing process discovery algorithms have proven to construct models of high quality on clean logs of structured processes, it still remains a challenge when the algorithms are being applied to logs of complex processes. The creation of a multi-level, hierarchical representation of a process can help to manage this complexity. However, current approaches that pursue this idea suffer from a variety of weaknesses. In particular, they do not deal well with interleaving subprocesses. In this paper, we propose FlexHMiner, a three-step approach to discover processes with multi-level interleaved subprocesses. We implemented FlexHMiner in the open source Process Mining toolkit ProM. We used seven real-life logs to compare the qualities of hierarchical models discovered using domain knowledge, random clustering, and flat approaches. Our results indicate that the hierarchical process models that the FlexHMiner generates compare favorably to approaches that do not exploit hierarchy.

Tim K. Smit, Hajo A. Reijers, Xixi Lu

Predictive Process Monitoring focuses on predicting future states of ongoing process executions, such as forecasting the remaining time. Recent developments in Object-Centric Process Mining have enriched event data with objects and their explicit relations between events. To leverage this enriched data, we propose the Heterogeneous Object Event Graph encoding (HOEG), which integrates events and objects into a graph structure with diverse node types. It does so without aggregating object features, thus creating a more nuanced and informative representation. We then adopt a heterogeneous Graph Neural Network architecture, which incorporates these diverse object features in prediction tasks. We evaluate the performance and scalability of HOEG in predicting remaining time, benchmarking it against two established graph-based encodings and two baseline models. Our evaluation uses three Object-Centric Event Logs (OCELs), including one from a real-life process at a major Dutch financial institution. The results indicate that HOEG competes well with existing models and surpasses them when OCELs contain informative object attributes and event-object interactions.

Soobin Chae, Suhwan Lee, Hanna Hauptmann et al.

Predictive Process Monitoring (PPM) often uses deep learning models to predict the future behavior of ongoing processes, such as predicting process outcomes. While these models achieve high accuracy, their lack of interpretability undermines user trust and adoption. Explainable AI (XAI) aims to address this challenge by providing the reasoning behind the predictions. However, current evaluations of XAI in PPM focus primarily on functional metrics (such as fidelity), overlooking user-centered aspects such as their effect on task performance and decision-making. This study investigates the effects of explanation styles (feature importance, rule-based, and counterfactual) and perceived AI accuracy (low or high) on decision-making in PPM. We conducted a decision-making experiment, where users were presented with the AI predictions, perceived accuracy levels, and explanations of different styles. Users' decisions were measured both before and after receiving explanations, allowing the assessment of objective metrics (Task Performance and Agreement) and subjective metrics (Decision Confidence). Our findings show that perceived accuracy and explanation style have a significant effect.

Jan Martijn E. M. van der Werf, Artem Polyvyanyy, Bart R. van Wensveen et al.

The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world process well. Intuitively, the better the quality of the event data, the better the quality of the model that is discovered. However, existing process discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We will also use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic process discovery.

Marcus Dees, Massimiliano de Leoni, Wil M. P. van der Aalst et al.

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.

Seyed Amin Tabatabaei, Xixi Lu, Mark Hoogendoorn et al.

Grouping patients meaningfully can give insights about the different types of patients, their needs, and the priorities. Finding groups that are meaningful is however very challenging as background knowledge is often required to determine what a useful grouping is. In this paper we propose an approach that is able to find groups of patients based on a small sample of positive examples given by a domain expert. Because of that, the approach relies on very limited efforts by the domain experts. The approach groups based on the activities and diagnostic/billing codes within health pathways of patients. To define such a grouping based on the sample of patients efficiently, frequent patterns of activities are discovered and used to measure the similarity between the care pathways of other patients to the patients in the sample group. This approach results in an insightful definition of the group. The proposed approach is evaluated using several datasets obtained from a large university medical center. The evaluation shows F1-scores of around 0.7 for grouping kidney injury and around 0.6 for diabetes.

Jan Mendling, Ingo Weber, Wil van der Aalst et al.

Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM.

Jan Claes, Irene Vanderfeesten, Hajo A. Reijers et al.

In an investigation into the process of process modeling, we examined how modeling behavior relates to the quality of the process model that emerges from that. Specifically, we considered whether (i) a modeler's structured modeling style, (ii) the frequency of moving existing objects over the modeling canvas, and (iii) the overall modeling speed is in any way connected to the ease with which the resulting process model can be understood. In this paper, we describe the exploratory study to build these three conjectures, clarify the experimental set-up and infrastructure that was used to collect data, and explain the used metrics for the various concepts to test the conjectures empirically. We discuss various implications for research and practice from the conjectures, all of which were confirmed by the experiment.

Jan Claes, Irene Vanderfeesten, Jakob Pinggera et al.

In the quest for knowledge about how to make good process models, recent research focus is shifting from studying the quality of process models to studying the process of process modeling (often abbreviated as PPM) itself. This paper reports on our efforts to visualize this specific process in such a way that relevant characteristics of the modeling process can be observed graphically. By recording each modeling operation in a modeling process, one can build an event log that can be used as input for the PPMChart Analysis plug-in we implemented in ProM. The graphical representation this plug-in generates allows for the discovery of different patterns of the process of process modeling. It also provides different views on the process of process modeling (by configuring and filtering the charts).

Jan Claes, Irene Vanderfeesten, Jakob Pinggera et al.

The construction of business process models has become an important requisite in the analysis and optimization of processes. The success of the analysis and optimization efforts heavily depends on the quality of the models. Therefore, a research domain emerged that studies the process of process modeling. This paper contributes to this research by presenting a way of visualizing the different steps a modeler undertakes to construct a process model, in a so-called process of process modeling Chart. The graphical representation lowers the cognitive efforts to discover properties of the modeling process, which facilitates the research and the development of theory, training and tool support for improving model quality. The paper contains an extensive overview of applications of the tool that demonstrate its usefulness for research and practice and discusses the observations from the visualization in relation to other work. The visualization was evaluated through a qualitative study that confirmed its usefulness and added value compared to the Dotted Chart on which the visualization was inspired.

Jakob Pinggera, Pnina Soffer, Stefan Zugal et al.

Research on quality issues of business process models has recently begun to explore the process of creating process models. As a consequence, the question arises whether different ways of creating process models exist. In this vein, we observed 115 students engaged in the act of modeling, recording all their interactions with the modeling environment using a specialized tool. The recordings of process modeling were subsequently clustered. Results presented in this paper suggest the existence of three distinct modeling styles, exhibiting significantly different characteristics. We believe that this finding constitutes another building block toward a more comprehensive understanding of the process of process modeling that will ultimately enable us to support modelers in creating better business process models.