In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become more complex. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the comprehension of complex process models. We present different methods for abstracting business process models into a format accessible to LLMs, and we implement advanced prompting strategies specifically designed to optimize LLM performance within our framework. Additionally, we present a tool, AIPA, that implements our proposed framework and allows for conversational process querying. We evaluate our framework and tool through: i) an automatic evaluation comparing different LLMs, model abstractions, and prompting strategies; ii) a qualitative analysis assessing the ability to identify critical quality issues in process models; and iii) a user study designed to assess AIPA’s effectiveness comprehensively. Results demonstrate our framework’s ability to improve the comprehension and understanding of process models, pioneering new pathways for integrating AI technologies into the BPM field. • Cutting-edge LLMs can effectively interpret abstractions of business process models. • Prompting techniques enhance the LLMs’ comprehension of process models. • Recent open-source LLMs demonstrate comparable effectiveness to commercial solutions. • Advanced LLMs can identify critical quality issues in process models, such as deadlocks and soundness violations. • A case study shows promising results with suggestions for more concise responses.

This article aims to foster a collaborative environment between the visual analytics and process mining communities by bringing together analysis methods, techniques, and tools from the process mining and visual analytics domains to devise a new knowledge-assisted, human-in-the-loop approach to process mining. Building on recent advances in methods emphasizing the role of human knowledge in analysis, we introduce knowledge-assisted interactive visual process mining (KAVA-PM) as a framework where analysts’ tacit knowledge and the externalizations of this knowledge play a key role. To achieve this, we extend an established conceptual model of KAVA that combines interactive visualizations and automated methods to support a richer process mining analysis practice that has human experts and their knowledge at its core. The paper outlines the key components of KAVA-PM as a conceptual model and its relations, proposes key analytical patterns, and demonstrates the use and validity of the patterns through usage scenarios. We then present challenges and open problems which we validate through a survey with experts. We anticipate that along with the conceptual model, these challenges will bring the VA and PM communities together along a shared research agenda where the role of humans and their knowledge is better established. • Conceptual model adapted to process mining for distinguishing between tacit and explicit knowledge. • Key research challenges validated by the visual analytics and process mining communities through an international survey.

Operational processes often exhibit concurrency, where the execution of activities can overlap in time. Moreover, repetitions of activities, both intentional (e.g., iterative tasks) and unintentional (e.g., rework) often occur. Existing process mining techniques and visualizations largely assume sequential event data, making it difficult to analyze repetitions in partially ordered event data, which better captures real-world process behavior. We address this gap by introducing a novel arc-diagram-based visualization that highlights recurring activity patterns within individual process execution variants. This approach allows analysts to intuitively detect repetitions that are otherwise obscured in raw data or traditional variant views. We validate the usefulness and ease of use of the proposed visualization through a user study with process mining experts and provide an implementation of our contribution in an open-source tool, supporting practical adoption.

In many real-world scenarios, processes naturally define partial orders over their constituent tasks. Partially ordered representations can be exploited in process discovery as they facilitate modeling such processes. The Partially Ordered Workflow Language (POWL) extends partially ordered representations with control-flow operators to support modeling common process constructs such as choice and loop structures. POWL integrates the hierarchical nature of process trees with the flexibility of partially ordered representations, opening up significant opportunities in process discovery. This paper presents and compares various approaches for the automated discovery of POWL models. We investigate the effects of applying varying validity criteria to partial orders, and we propose methods for incorporating frequency information to improve the quality of the discovered models. Additionally, we propose alternative visualizations for POWL models, offering different approaches that may be useful in various contexts. The discovery approaches are evaluated using various real-life data sets, demonstrating the ability of POWL models to capture complex process structures. • Employing different validity requirements in the discovery of POWL models. • Incorporating frequency-based filtering in the discovery of POWL models. • Enhancing the visualization of the discovered models. • Proving the soundness of the discovered models.

The execution of operational processes generates event data stored in enterprise information systems. Process mining techniques analyze such event data to obtain insights vital for decision-makers to improve the reviewed process. In this context, event data visualizations are essential. We focus on visualizing variants describing process executions that are control flow equivalent. Such variants are an integral concept for process mining and are used, e.g., for data exploration and filtering. We propose high-level and low-level variants covering different levels of abstraction and present corresponding visualizations. Compared to existing variant visualizations, we support partially ordered event data and allow for heterogeneous temporal information per event, i.e., we support both time intervals and time points. We evaluate our contributions using automated experiments showing practical applicability to real-life event data. Finally, we present a user study indicating significantly improved usefulness and ease of use of the proposed high-level variant visualization compared to existing variant visualizations for typical analysis tasks.

Many real-life processes naturally define partial orders over the activities they are composed of. Partial orders can be used as a graph-like representation of process behavior, allowing us to model concurrent and sequential dependencies. The Partially Ordered Workflow Language (POWL) combines block-structured modeling notations with partially-ordered graph representations. A POWL model is a hierarchical model where sub-models can be combined into a new model either using a control-flow operator or as a partial order. The application of POWL models in process mining remains a challenge due to a lack of scalable approaches for the discovery of POWL models. In this paper, we address this gap by proposing an approach for the discovery of POWL models that leverages large data sets and ensures high conformity with the input data. Our approach provides formal guarantees on the uniqueness, existence, and quality of discovered partial orders. The evaluation of our approach underscores its scalability with large data sets and its ability to generate high-quality models.

Event logs, as considered in process mining, document a large number of individual process executions. Moreover, each process execution consists of various executed activities. To cope with the vast amount of process executions in event logs, the concept of variants exists that group process executions with identical ordering relations among their executed activities. Variants are an integral concept of process mining and help process analysts explore, filter, and manage large amounts of event data. In this paper, we consider concurrency-aware variants that allow activities within a process execution to be partially ordered---the execution of individual activities can overlap in time. However, the number of variants is often vast, making it challenging for process analysts to explore event data. Therefore, we present a novel approach to frequent pattern mining from concurrency-aware variants. We show that mining frequent patterns from concurrency-aware variants can be reduced to the frequent subtree mining problem. Further, we compare our proposed algorithm to a state-of-the-art frequent subtree mining algorithm exhibiting improved performance on real-life event logs.

The execution of processes leaves traces of event data in information systems. These event data can be analyzed through process mining techniques. For traditional process mining techniques, one has to associate each event with exactly one object, e.g., the company’s customer. Events related to one object form an event sequence called a case. A case describes an end-to-end run through a process. The cases contained in event data can be used to discover a process model, detect frequent bottlenecks, or learn predictive models. However, events encountered in real-life information systems, e.g., ERP systems, can often be associated with multiple objects. The traditional sequential case concept falls short of these so-called object-centric event data since these data exhibit a graph structure. One might force object-centric event data into the traditional case concept by flattening it. However, flattening manipulates the data and removes information. Therefore, a concept analogous to the case concept of traditional event logs is necessary to enable the application of different process mining tasks on object-centric event data. In this paper, we introduce the case concept for object-centric process mining: process executions. These are graph-based generalizations of cases as considered in traditional process mining. Furthermore, we provide techniques to extract process executions. Based on these executions, we determine equivalent process behavior with respect to an attribute using graph isomorphism. Equivalent process executions with respect to the event’s activity are object-centric variants, i.e., a generalization of variants in traditional process mining. We provide a visualization technique for object-centric variants. The contribution’s scalability and efficiency are extensively evaluated. Furthermore, we provide a case study showing the most frequent object-centric variants of a real-life event log. Our contributions might be used as a basis to adapt traditional process mining techniques by researchers and to generate initial control-flow insights into object-centric event logs by practitioners.