Matthias Weidlich

Process mining analyzes processes based on event logs, which are generally assumed to be complete and trustworthy. These assumptions are often violated in real-world contexts, raising uncertainty about result reliability. Uncertainty-aware process mining promises to address this by explicitly leveraging uncertainty in calculations. We perform a structured survey of uncertainty-aware process mining (34 papers), which illustrates the currently dominant conceptualization of uncertainty in process mining and reveals the need for a conceptual foundation and integration with related research that handles uncertainty implicitly (e.g., process data quality). To address this, we construct an integrative conceptual framework for uncertainty in process mining by extending the core survey findings through an iterative hermeneutic review of seminal works from related research. The framework consists of two complementary lenses, detailing the (i) possible sources of and (ii) viable treatment strategies for uncertainty. Application of the framework shows both its integrative power for (future) research and its utility for guiding the handling of uncertainty in practical application.

Process mining provides a broad portfolio of methods for extracting insights from event data. Many of these methods can benefit from incorporating semantic relations between activities. A common way to capture such relations is distributional similarity, which links activities based on the contexts in which they occur. Distributional information can be encoded in activity embeddings learned either by prediction-based approaches (adapting neural models from natural language processing) or by count-based models. In prior work, we showed that simple count-based embeddings can match or even outperform neural prediction-based methods for assessing activity similarity in event data. This finding, together with the limited interpretability and high computational cost of neural approaches, motivates the use of count-based alternatives. However, existing distributional similarity methods are limited to certain event data, where each event is assigned a single activity label. In this paper, we argue that many contemporary process mining applications also involve uncertain event data, where events are associated with multiple possible activities and corresponding likelihoods. We generalize count-based approaches and adapt state-of-the-art neural network approaches to the domain of uncertain event data. For both count-based and neural network approaches, probabilistic evidence is incorporated to provide a more robust and comprehensive similarity assessment. Finally, we contribute a comprehensive benchmarking framework for distributional similarity between activities on both certain and uncertain event data, evaluating (i) intrinsic similarity quality, (ii) performance in next-activity prediction as a downstream task, and (iii) computational efficiency. Across 28 certain and 9 uncertain event logs, count-based activity-activity embeddings achieve the highest intrinsic similarity quality overall and allow a simple activity-only predictor to match the state-of-the-art Tax model in next-activity prediction without temporal features, while running more than an order of magnitude faster than neural alternatives on uncertain data. Embedding quality is task-dependent: activity-activity representations lead in intrinsic retrieval while activity-context representations lead in downstream prediction.

Scientific Workflows (SWF) encapsulate data processing tasks by organizing various tools, operators, and data in a logical flow. Due to their complex and domain-specific nature, developing SWFs remains laborious. Current code-generating large language models (Code LLMs) struggle to assist users in developing these workflows. This limitation arises primarily from the insufficient availability of relevant training data in public repositories, making it challenging for LLMs to learn specialized patterns and domain-specific logic. To address this, we propose FlowPilot , a suggestion framework for developing SWFs that assists developers by suggesting the next operator. Our system complements Code LLMs by enriching the source code to generate more accurate results. FlowPilot leverages a similarity knowledge base (SKB) that indexes historical workflows to find the ones matching the current context. To generate relevant suggestions, FlowPilot employs a statistical approach based on Markov chains to identify the most likely next step. As a proof of concept, we evaluated our system on NextFlow workflows and the results demonstrate the effectiveness of FlowPilot . It outperforms state-of-the-art code-generating models, e.g., Llama-4, and traditional methods, e.g., association rule mining techniques.

Process mining traditionally assumes centralized event data collection and analysis. However, modern Industrial Internet of Things systems increasingly operate over distributed, resource-constrained edge-cloud infrastructures. This paper proposes a structured approach for decentralizing process mining by enabling event data to be mined directly within the IoT systems edge-cloud continuum. We introduce ContinuumConductor a layered decision framework that guides when to perform process mining tasks such as preprocessing, correlation, and discovery centrally or decentrally. Thus, enabling privacy, responsive and resource-efficient process mining. For each step in the process mining pipeline, we analyze the trade-offs of decentralization versus centralization across these layers and propose decision criteria. We demonstrate ContinuumConductor at a real-world use-case of process optimazition in inland ports. Our contributions lay the foundation for computing-aware process mining in cyber-physical and IIoT systems.

Over the past decade, the proliferation of public and enterprise data lakes has fueled intensive research into data discovery, aiming to identify the most relevant data from vast and complex corpora to support diverse user tasks. Significant progress has been made through the development of innovative index structures, similarity measures, and querying infrastructures. Despite these advances, a critical aspect remains overlooked: relevance is time-varying. Existing discovery methods largely ignore this temporal dimension, especially when explicit date/time metadata is missing. To fill this gap, we outline a vision for a data discovery system that incorporates the temporal dimension of data. Specifically, we define the problem of temporally-valid data discovery and argue that addressing it requires techniques for version discovery, temporal lineage inference, change log synthesis, and time-aware data discovery. We then present a system architecture to deliver these techniques, before we summarize research challenges and opportunities. As such, we lay the foundation for a new class of data discovery systems, transforming how we interact with evolving data lakes.

Business process management is increasingly practiced using data-driven approaches. Still, classical imperative process models, which are typically formalized using Petri nets, are not straightforwardly applicable to the relational databases that contain much of the available structured process execution data. This creates a gap between the traditional world of process modeling and recent developments around data-driven process analysis, ultimately leading to the under-utilization of often readily available process models. In this paper, we close this gap by providing an approach for translating imperative models into relaxed process data queries, specifically SQL queries executable on relational databases, for conformance checking. Our results show the continued relevance of imperative process models to data-driven process management, as well as the importance of behavioral footprints and other declarative approaches for integrating model-based and data-driven process management.

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.