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.

Alignments are a well-known process mining technique for reconciling system logs and normative process models. Evidence of certain behaviors in a real system may only be present in one representation – either a log or a model – but not in the other. Since processes involve multiple entities, such as objects and resources performing different tasks with objects, the interaction of these entities must be taken into account in the alignments. Additionally, both logged and modeled representations of reality may be imprecise and only partially represent some of these entities, but not all. In this paper, we introduce the concept of “relaxations” through projections for alignments to deal with partially correct models and logs. Relaxed alignments help to distinguish between trustworthy and untrustworthy content of the two representations (the log and the model) to achieve a better understanding of the underlying process and expose quality issues.

Efficient hospital discharge planning is crucial for maintaining patient flow. At St. Antonius Hospital in the Netherlands, delays in aftercare planning lead to prolonged hospital stays, placing unnecessary burden on resources. This study investigates the use of machine learning to improve predictions of both remaining length of stay and aftercare needs, aiming to align care and transfer processes. The key contribution of this paper is a new method to train prediction models for the remaining length of stay and aftercare needs in such a way that they perform better on clinical impact rather than standard metrics, when evaluated jointly. These predictions are essential for efficient discharge planning. Our findings reveal that models trained using conventional metrics partly fail to reduce these inefficiencies, while those optimized on cost and clinical outcomes reduce prolonged stays.

Process mining enables organizations to gain datadriven insights into their business processes. An increasing number of organizations are either launching process mining initiatives or expanding the current scope and areas of application. Despite the growing adoption of process mining among practitioners, several challenges remain, such as the lack of clear value propositions. While recent studies have examined factors influencing value identification, little attention has been given to what affects individual business users in generating insights from process mining. As value creation is driven by the business user of process mining output, it is essential that they are able to make sense and interpret its outputs into actionable insights. In this paper, we present the results of an interview study with process mining business users. Based on the interview data, we derive the factors that influence the use of process mining outputs in order to obtain insights. We report on the factors grouped into three main categories: (1) process mining knowledge, (2) tooling and visualization, and (3) business and domain knowledge. We then discuss practical implications of these factors for practitioners, highlighting both PM output design-related considerations and contextual factors, such as user training and clearly defined analysis goals, that influence how process mining outputs are interpreted and used.

The assessment of process mining techniques using real-life data is often compromised by the lack of ground truth knowledge, the presence of non-essential outliers in system behavior and recording errors in event logs. Using synthetically generated data could leverage ground truth for better evaluation. Existing log generation tools inject noise directly into the logs, which does not capture many typical behavioral deviations. Furthermore, the link between the model and the log, which is needed for later assessment, becomes lost.We propose a ground-truth approach for generating process data from either existing or synthetic initial process models, whether automatically generated or hand-made. This approach incorporates patterns of behavioral deviations and recording errors to produce a synthetic yet realistic deviating model and imperfect event log. These, together with the initial model, are required to assess process mining techniques based on ground truth knowledge. We demonstrate this approach with a conformance checking use case, focusing on (relaxed) systemic alignments to expose and explain deviations in modeled and recorded behavior. Our results show that this approach, unlike traditional methods, provides detailed insights into the strengths and weaknesses of process mining techniques, both quantitatively and qualitatively.

Conformance checking (CC) techniques in process mining determine the conformity of cases, by means of their event sequences, with respect to a business process model. Online conformance checking (OCC) techniques perform such analysis for cases in event streams. Cases in streams may essentially not be concluded. Therefore, OCC techniques usually neglect the memory limitation and store all the observed cases whether seemingly concluded or unconcluded. Such indefinite storage of cases is inconsistent with the spirit of privacy regulations, such as GDPR, which advocate the retention of minimal data for a definite period of time. Catering to the aforementioned constraints, we propose two classes of novel approaches that partially or fully forget cases but can still properly estimate the conformance of their future events. All our proposed approaches bound the number of cases in memory and forget those in excess of the defined limit on the basis of prudent forgetting criteria. One class of these proposed approaches retains a meaningful summary of the forgotten events in order to resume the CC of their cases in the future, while the other class leverages classification for this purpose. We highlight the effectiveness of all our proposed approaches compared to a state of the art OCC technique lacking any forgetting mechanism through experiments using real-life as well as synthetic event data under a streaming setting. Our approaches substantially reduce the amount of data required to be retained while minimally impacting the accuracy of the conformance statistics.

Process mining can be used to analyze business processes based on logs of their execution. These execution logs are often obtained by querying a database and storing the results in a file. The mining itself is then done on the file, such that the data processing power of the database cannot be used after the log is extracted. Enabling process mining directly on a database therefore provides additional flexibility and efficiency. To help facilitate this, this paper formally defines a database operator that extracts the ‘directly follows’ relation—one of the relations that is at the heart of process mining—from an operational database. It defines the operator using the well-known relational algebra and formally proves equivalence properties of the operator that are useful for query optimization. Subsequently, it presents time-complexity properties of the operator. Finally, it presents an implementation of the operator as part of the H2 DBMS and demonstrates that this implementation extracts the ‘directly follows’ relation from a database with an arbitrary database structure within a fraction of a second; several orders of magnitude faster than is currently possible.

Alignments are a fundamental approach in conformance checking to provide an explicit relation between traces of events observed in an event log and execution sequences of process models. They are robust against intricacies in process models such as duplicate labels and invisible transitions, but at the same time computing them is a time consuming task. In this paper, we argue that precomputed rules may be leveraged to improve on the time needed to compute alignments. To this end, we utilize both structural and behavioral properties of process models to derive rules and we compare events against these rules. A violation in one of the rules indicates a problem in the event. Before alignments are computed, we mark the problematic events as so-called splitpoints. We evaluated this approach on real-life logs as well as benchmarking logs, and the results show that the proposed approach is faster than existing alignment approaches.

The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper, we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining toolkit ProM ( http://promtools.org ). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.

Process discovery algorithms typically aim at discovering process models from event logs that best describe the recorded behavior. Often, the quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. replay fitness. At the same time, there are other measures that compare a model with recorded behavior in terms of the precision of the model and the extent to which the model generalizes the behavior in the log. Furthermore, many measures exist to express the complexity of a model irrespective of the log. In this paper, we first discuss several quality dimensions related to process discovery. We further show that existing process discovery algorithms typically consider at most two out of the four main quality dimensions: replay fitness, precision, generalization and simplicity. Moreover, existing approaches cannot steer the discovery process based on user-defined weights for the four quality dimensions. This paper presents the ETM algorithm which allows the user to seamlessly steer the discovery process based on preferences with respect to the four quality dimensions. We show that all dimensions are important for process discovery. However, it only makes sense to consider precision, generalization and simplicity if the replay fitness is acceptable.