Business process management employs process models and event logs to represent the behavior of the information systems under study. Traditional case-centric notions consider the order of activities and events in isolated process instances. The emerging field of object-centric processes challenges this assumption by putting objects in the center. Object-centric process mining and modeling approaches identify the structure of co-evolving data objects that influence the behavior of an information system to provide a comprehensive view of the system behavior. Object-centricity has been investigated independently in process modeling and in process mining, which resulted in the coexistence of seemingly contradictory assumptions and definitions. As a community effort, this research manifesto relates and aligns existing terminologies, definitions, and perspectives to provide a common ground for current and future research in object-centric business process management. Based on the current state of research, we propose a conceptualization that sets process models and event logs in relation to the information system’s behavior and the execution data it generates. The conceptualization aims at aligning different terminologies and, thus, providing a basis to model and analyze behavioral characteristics. Building on this common ground, we identify open research challenges along the most relevant research areas in object-centric process management. For each research area, its current status is investigated and an outline of the most relevant research challenges is presented.

Process execution time is a key performance indicator for evaluating bottlenecks in business processes. Cases and activities that exceed the specified time constraints can be seen as anomalies, affecting process performance and leading to risks such as delays and customer complaints. Identifying the root causes of these anomalies can help formulate effective intervention measures. However, this task is inherently complex, and conducting incomplete or inaccurate analysis can result in misguided interventions that inadvertently exacerbate process inefficiencies. To address these challenges, this paper proposes a traceability-based root cause analysis approach for process performance anomalies using causal inference. Specifically, the approach begins by extracting hidden contextual information from the event log to enrich the pool of potential causal factors. Then formulates causal hypotheses linking these factors to observed performance anomalies (at both the case and activity level) and establishes potential causal relations through a traceability mechanism. A meta-learning based causal inference approach is used to estimate the strength of causal effects. The proposed approach is evaluated against a state-of-the-art approach using four synthetic event logs with known root causes and nine public real-life event logs. Experimental results demonstrate that the proposed approach delivers accurate insights into the root causes of process performance anomalies in synthetic event logs, while maintaining high efficiency in the comprehensive analysis of potential causal factors.

To obtain insights from event data, advanced process mining methods assess the similarity of activities to incorporate their semantic relations into the analysis. Here, distributional similarity that captures similarity from activity co-occurrences is commonly employed. However, existing work for distributional similarity in process mining adopt neural network-based approaches as developed for natural language processing, e.g., word2vec and autoencoders. While these approaches have been shown to be effective, their downsides are high computational costs and limited interpretability of the learned representations. In this work, we argue for simplicity in the modeling of distributional similarity of activities. We introduce count-based embeddings that avoid a complex training process and offer a direct interpretable representation. To underpin our call for simple embeddings, we contribute a comprehensive benchmarking framework, which includes means to assess the intrinsic quality of embeddings, their performance in downstream applications, and their computational efficiency. In experiments that compare against the state of the art, we demonstrate that count-based embeddings provide a highly effective and efficient basis for distributional similarity between activities in event data.

Prescriptive process monitoring aims to derive recommendations for optimizing complex processes. While previous studies have successfully used reinforcement learning techniques to derive actionable policies in business processes, care processes present unique challenges due to their dynamic and multifaceted nature. For example, at any stage of a care process, a multitude of actions is possible. In this study, we follow the Reinforcement Learning (RL) approach and present a general approach that uses event data to build and train Markov decision processes. We proposed three algorithms including one that takes the elapsed time into account when transforming an event log into a semi-Markov decision process. We evaluated the RL approach using an aggression incident data set. Specifically, the goal is to optimize staff member actions when clients are displaying different types of aggressive behavior. The Q-learning and SARSA are used to find optimal policies. Our results showed that the derived policies align closely with current practices while offering alternative options in specific situations. By employing RL in the context of care processes, we contribute to the ongoing efforts to enhance decision-making and efficiency in dynamic and complex environments.

Predictive Process Monitoring (PPM) aims to predict the future state of running process instances to enable timely interventions to mitigate potential risks. As one of the most fundamental tasks in PPM, process remaining time prediction focuses on preventing timeout occurrences. While various deep learning-based approaches have been developed for this purpose, they often rely on pre-established static prediction models and struggle to maintain accurate predictions when the process undergoes dynamic changes, such as an expanding sales channels. To tackle this challenge, this paper proposes an incremental process remaining time prediction framework by continuously updating the prediction model based on an incremental event log. Specifically, a feature selection strategy is first introduced to extract effective features from event logs. Leveraging effective features can significantly improve the prediction quality by capturing the changes in process information. Then, three incremental log-based updating mechanisms, including period-based updating, quantity-based updating, and concept-drift-based updating, along with a reconstruction strategy, are proposed to dynamically adjust the prediction model in response to business changes. Finally, LSTM, Transformer, and Auto-encoder models are adapted and integrated into the proposed framework. The approach has been implemented and publicly released. Experimental evaluation using nine real-life event logs demonstrate that the proposed framework and its three instantiations (i.e., LSTM-based, Transformer-based, and Auto-encoder-based ones) outperform state-of-the-art techniques in terms of prediction accuracy.

Predictive Process Monitoringaims to predict the future information of ongoing process executions by leveraging machine and deep learning techniques. One of the tasks is known asremaining time prediction, which focuses on predicting the remaining time of ongoing cases. Accurate remaining time prediction can be valuable and important for improving business operations or taking timely interventions to prevent delays. For predicting the remaining time, existing work has used deep learning techniques to achieve high prediction accuracy. However, most of these techniques tend to learn very complex models that are difficult to explain. Systematic feature selection approaches may help improve both the prediction accuracy and the explainability of the model. In this paper, we introduce a feature-informed cascade prediction framework to predict the remaining time. Specifically, we first propose an approach that builds a tree of features by systematically estimating their effects on the remaining time prediction. Next, we use the tree to either automatically select an optimal combination of features or to guide users in this selection process. Each selected feature is correlated with its prediction results in our Feature-informed Cascade Prediction Model (FCPM) for explainability. The proposed approach has been implemented and is made publicly available. Using eight public real-life event logs, the proposed approach is compared to the state-of-the-art approaches in terms of prediction accuracy. In addition, it is demonstrated that our approach visualizes the impact of each input feature in the prediction of individual cases, producing explanations of the prediction results.

The field of process mining has evolved from discovering single work processes towards providing broad insights into peoples’ work practices. Existing techniques can be used to analyse such work practices, but this can be problematic if the available data is limited to the use of a single IT system or is not captured at the right level of granularity. We propose the use of a personal informatics technique, called Active Window Tracking (AWT), as a new way of gathering data for mining work practices. In this study, we identify the opportunities that this technique brings through a case study within our research group. In particular, we show how AWT helps to: capture previously-unrecorded work activities, expose the relations between work processes, and navigate between different levels of data granularity. The technique, which allows for generating new data as well as complementing existing data, is a valuable asset for the community when it comes to better understanding people’s work practices across individual systems and processes.

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.

Abstract The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [ t , t ], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms.

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.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

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.

In this work, we propose FlexHMiner (FH), a three-step approach for the discovery of hierarchal models. We formalize the concept of activity tree and event abstraction, which allows us to be flexible in the ways of computing the process hierarchy. We illustrate this flexibility by proposing three different techniques to discover an activity tree: (1) a fully domain based approach (DK-FH), (2) a random approach (RC-FH), and (3) a fall-back, flat activity tree (F-FH). After obtaining an activity tree, the second step of our approach is to compute the logs for each subprocess using log abstraction and log projection. Finally, FlexHMiner discovers a subprocess model for each subprocess by leveraging the capabilities of existing discovery algorithms. Using the domain-based approach as the gold standard and the flat tree approach as base line, we compare the three ways of discovering an activity tree using seven real-life logs.

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 set of positive examples given by domain experts. 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.

Enterprise Resource Planning (ERP) systems are widely used to manage business documents along a business processes and allow very detailed recording of event data of past process executions and involved documents. This recorded event data is the basis for auditing and detecting unusual flows. Process mining techniques can analyze event data of processes stored in linear event logs to discover a process model that reveals unusual executions. Existing approaches to obtain linear event logs from ERP data require a single case identifier to which all behavior can be related. However, in ERP systems processes such as Order to Cash operate on multiple interrelated business objects, each having their own case identifier, their own behavior, and interact with each other. Forcing these into a single case creates ambiguous dependencies caused by data convergence and divergence which obscures unusual flows in the resulting process model. In this paper, we present a new semi-automatic, end-to-end approach for analyzing event data in a plain database of an ERP system for unusual executions. More precisely, we identify an artifact-centric process model describing the business objects, their life-cycles, and how the various objects interact along their life-cycles. This way, we prevent data divergence and convergence. We report on two case studies where our approach allowed to successfully analyze processes of ERP systems and reliably revealed unusual flows later confirmed by domain experts.

There is a continuous pressure to make healthcare processes more efficient and effective without sacrificing quality. Conformance checking can be used to improve processes by analyzing event data and directly relating observed behavior and modeled behavior. Conformance checking provides diagnostics that go far beyond measuring traditional key performance indicators. However, current conformance checking techniques focus on a rather simplistic setting where executions of process instances are sequential and homogeneous whereas healthcare processes are known to be dynamic, complex, and ad-hoc. In healthcare process instances of patients often follow a unique path through the process with one-of-a-kind deviations. Moreover, timestamps are often rather coarse (the date is known, but not the time) resulting in an unreliable ordering of events. As current techniques are unable to handle concurrent events, and the obtained sequential alignments are unable to provide structural information about deviations, the diagnostics provided are often insufficient and misleading. This paper presents a novel approach using partially ordered traces and partially ordered alignments which aims to incorporate unreliability and concurrency in the input while providing diagnostics about deviations that take causalities into account. The approach has been implemented in ProM and was evaluated using event data from a Dutch hospital.