Collaboration processes in non-process-aware information systems are inherently ad hoc and exhibit substantial structural variability, which limits the applicability of traditional process mining. Social Process Mining addresses this gap by providing tailored methods for the discovery of collaboration patterns. In a nutshell, collaborative work between employees can be expressed through small graph‐based process models that are built from event logs. These models provide a basis for extracting subgraphs, which are then labeled as collaboration patterns. Collaboration patterns can be extracted from process models, for instance, using Frequent Subgraph Mining (FSM). However, small deviations in activity order or execution context often prevent meaningful collaboration patterns from being detected by traditional exact FSM algorithms. To address this challenge and extract meaningful collaboration patterns, we propose a context-aware relaxed FSM approach for directed, typed, and labeled process model graphs. Our algorithm relaxes strict FSM by employing similarity-based matching while simultaneously enforcing constraints that preserve complete contextual information for all activity nodes. The approach follows a two-step algorithm consisting of constrained candidate generation and similarity-based candidate matching with configurable support and relaxation thresholds. We evaluate the approach using call center collaboration event data and event data from an Enterprise Collaboration System. The results show that our algorithm produces semantically rich and meaningful collaboration patterns and successfully identifies frequent structures that exact matching fails to detect. Our findings demonstrate the effectiveness of combining relaxed matching with explicit context constraints for collaboration pattern detection.

When declarative process models (DPMs) contain contradictory constraints, they immediately become unsatisfiable. Recognizing the challenges of automated inconsistency resolution approaches in real-world scenarios, we present an interactive and comprehensive approach for resolving inconsistencies in DPMs. Our approach targets both design-time and run-time inconsistencies through incremental resolution of inconsistency cores. To guide users effectively and lower the mental effort required during the resolution process, we rank inconsistency cores based on customizable complexity measures. We also consider user preferences and familiarity with different inconsistency characteristics. Additionally, we dynamically integrate prior user decisions and resolution operations to ensure an efficient and user-centric approach. By presenting users with one inconsistency core at a time and providing decision support, our approach aims to minimize cognitive overload. Furthermore, it offers flexibility and adaptability by allowing users to apply their preferred resolution operations, reflecting the complexity of real-world scenarios.

Process Mining helps to analyze and interpret business processes using event logs of information systems as ground truth. It has been successfully applied to process-oriented enterprise systems but is less suited for communication- and document-oriented Enterprise Collaboration Systems (ECS). This is due to two core characteristics of ECS processes and process logging. On the one hand, ECS processes are highly unstructured as their usage is not related to predefined business processes. The usage of ECS is ad-hoc and flexible. On the other hand, ECS commonly provide rather low-level process logs, i.e., logged events are very fine-granular, and, thus hard to interpret. Both characteristics lead to so-called spaghetti process models, which are barely interpretable when applying “traditional” process mining to ECS. This work focuses on the second aspect, i.e., to solve the problem of too fine-granular process logs of ECS. A common solution and preprocessing step for this granularity challenge is event abstraction, i.e., converting low-level logs into more abstract high-level logs before applying further process mining analysis techniques. ECS logs come with special characteristics that have so far not been fully addressed by existing event abstraction approaches. We aim to close this gap with a tailored ECS event abstraction (ECSEA) approach that trains a model by comparing observed actual user activities (also named high-level traces) with the system-generated low-level traces. The model allows us to automatically convert historic and future low-level traces into abstracted high-level traces that can be used for Process Mining. This article describes the ECS event log characteristics, introduces the proposed ECSEA approach with the underlying algorithms, and shows a corresponding evaluation. Our two-fold evaluation shows that the algorithm produces accurate results. In the evaluation, we use both synthetic and real-world event data taken from a productive ECS with more than 4000 users currently running at a university.

In this case study, records about prostate cancer patients, provided by the Cancer Registry of Rhineland-Palatinate, Germany, are analyzed. The dataset is comparatively large and cases are rather complete, as they contain events gathered not only from one institution (e. g., a single hospital), but from multiple institutions along the end-to-end patient journey. The analysis, which aims at getting insights on prostate cancer treatment pathways and contributing to state-of-the-art research in the Process Mining for Healthcare (PM4H) field, is powered by methods and techniques from the process mining domain. Therefore, dealing with a process mining project, the PM^2 method was followed with the recommended phases in collaboration with the Cancer Registry of Rhineland-Palatinate, Germany. The initial analysis of ∼ 12k cases ( ∼ 90k events) recorded during 2018–2022 and considering only a small number of potential available data attributes already led to barely comprehensible spaghetti models, emphasizing the need for different views of granularity and complexity. This case study also provides results on the regular treatment pathways (such as surgery, or therapies).

The domain of Social Collaboration Analytics (SCA) is gathering momentum due to the increasing importance of Enterprise Collaboration Systems (ECS) that are used to build performant digital workplaces. ECS collect and store large amounts of digital trace data, which reflect the collaborative work of employees. Collaborative work activity is usually supported by multiple systems, which complicates the analysis of the digital traces. The cross-system analysis of collaborative work is challenging because of the nature of the log files that are stored in heterogeneous formats and at different levels of log granularity. These challenges cannot easily be overcome with the help of existing approaches for data preprocessing. In this paper, approaches and frameworks for data mining, data preprocessing and SCA are adapted to design a special method for Social Process Mining (SPM). The DaProXSA approach was developed to obtain interpretable, high-quality event logs of multiple ECS and builds on existing literature as well as on findings from an analysis of leading ECS. DaProXSA contributes to the aim of discovering and interpreting patterns and processes that occur in computer-mediated collaborative work.

While the Decision Model and Notation standard (DMN) is considered to be an increasingly popular standard, there is a broad consensus that human modelling errors can easily occur in the creation of DMN models. Yet, while this consensus is clear, there is only limited evidence of which error types exactly may occur in practice. In this work, we therefore present some empirical evidence on DMN errors in the wild. Specifically, we analyze the SAP-SAM dataset by SAP Signavio, containing over 500 000 real-world conceptual models. Our results show that modelling errors, such as missing rules, occur frequently in real-life settings (36.1% of all models contained some form of issue). Furthermore, we analyze the distribution of which error types have occurred (relative to an existing classification of DMN error types from a previous work). To the best of our knowledge, this is the largest DMN study conducted to date (N = 5 668 DMN models).

In contrast to procedural process models, where every valid execution trace is explicitly modeled, declarative process models (DPMs) implicitly define allowed company behavior using a set of constraints. Related works have identified many challenges humans face when trying to make sense of DPMs, including combinations of constraints, inconsistencies, and the (graphical) notation of declarative constraints themselves. In this work, we provide the foundation for an e-learning approach designed to gradually familiarize users with the modeling language Declare. More specifically, we introduce a comprehensive collection of different types of tasks with increasing levels of difficulty. These tasks cover basic concepts, individual constraints, constraint combinations of different complexity, model behavior, as well as redundancy and inconsistency within DPMs. With this work, we aim to lay the foundation for future interactive applications to support not only teaching but also improving both comprehension and (consistent) declarative process modeling.

Inconsistency handling in declarative process models (DPMs) has been of increased interest as even a single contradiction within a constraint set makes the entire DPM unsatisfiable. To develop interactive inconsistency resolution and prevention approaches, humans must be able to identify and understand the problem at hand. Therefore, we aim to gather first insights into the cognitive effects of inconsistency characteristics on understanding inconsistencies in DPMs by conducting an exploratory study. Our results show that participants had notable difficulties with understanding inconsistencies, which we could trace back to several inconsistency characteristics, such as combinations of interrelated constraints. Thus, we identified a strong need for the development of interactive and visual decision support technologies to improve inconsistency understanding in DPMs.

Modelling processes with declarative process models, i.e. sets of constraints, allows for a great degree of flexibility in process execution. However, having behavior specified by means of symbolic (textual) constraints comes along with the problem that it is often hard for humans to understand which exact behavior is allowed, and which is not (think for example of checking relationships between constraints). This becomes especially problematic when modellers need to carry out changes to a model. For example, a modeller must make sure that any alteration to a model does not introduce any unwanted or non-compliant behavior. As this is often difficult for humans, editing declarative process models currently bears the risk of (accidentally) inducing unforeseen compliance breaches due to some overlooked changes in behavior. In this work, we therefore present an approach to efficiently compute the behavioral changes between a declarative process model M and a corresponding (edited) model $$M'$$ . This supports modellers in understanding the behavioral changes induced by an alteration to the constraints. We implement our approach and show that behavioral changes can be computed within milliseconds even for real-life data-sets.