arXiv subjects

Hendrik Reiter

Publications and source records attributed to Hendrik Reiter.

8 recordsLinked to original sources

arXiv2026

The PM-EdgeMap: Towards Real-Time Process Mining on the Edge-Cloud Continuum

Smart factories are evolving into Cyber-Physical Systems (CPS), demanding increased autonomy. This necessitates real-time decision making, facilitated by insights derived from sensor data. Process mining offers a valuable approach to gain such insights and guide actions. The edge computing paradigm supports this real-time requirement by enabling network communication between sensors and leveraging nearby computing resources. This paper investigates the implications of performing real-time process mining algorithms on the edge. Within this paper, we first propose a formalism to describe relevant datasets and the computing topology. We then evaluate the edge computing approach through a case study involving an edge-based conformance checking algorithm. The results demonstrate the feasibility and benefits of edge-based real-time process mining for enhanced autonomous control in smart factories.

cs.DC↗

arXiv2026

Know Your Streams: On the Conceptualization, Characterization, and Generation of Intentional Event Streams

The shift toward IoT-enabled, sensor-driven systems has transformed how operational data is generated, favoring continuous, real-time event streams (ES) over static event logs. This evolution presents new challenges for Streaming Process Mining (SPM), which must cope with out-of-order events, concurrent activities, incomplete cases, and concept drifts. Yet, the evaluation of SPM algorithms remains rooted in outdated practices, relying on static logs or artificially streamified data that fail to reflect the complexities of real-world streams. To address this gap, we first perform a comprehensive review of data stream literature to identify stream characteristics currently not reflected in the SPM community. Next, we use this information to extend the conceptual foundation for ES. Finally, we propose Stream of Intent, a prototype generator to produce ES with specific features. Our evaluation shows excellence in producing reproducible, intentional ES for targeted benchmarking and adaptive algorithm development in SPM.

cs.DB↗

arXiv2025

ContinuumConductor : Decentralized Process Mining on the Edge-Cloud Continuum

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.

cs.DC↗

arXiv2025

Determining Window Sizes using Species Estimation for Accurate Process Mining over Streams

Streaming process mining deals with the real-time analysis of event streams. A common approach for it is to adopt windowing mechanisms that select event data from a stream for subsequent analysis. However, the size of these windows denotes a crucial parameter, as it influences the representativeness of the window content and, by extension, of the analysis results. Given that process dynamics are subject to changes and potential concept drift, a static, fixed window size leads to inaccurate representations that introduce bias in the analysis. In this work, we present a novel approach for streaming process mining that addresses these limitations by adjusting window sizes. Specifically, we dynamically determine suitable window sizes based on estimators for the representativeness of samples as developed for species estimation in biodiversity research. Evaluation results on real-world data sets show improvements over existing approaches that adopt static window sizes in terms of accuracy and robustness to concept drifts.

cs.DB↗

arXiv2025

AVOCADO: The Streaming Process Mining Challenge

Streaming process mining deals with the real-time analysis of streaming data. Event streams require algorithms capable of processing data incrementally. To systematically address the complexities of this domain, we propose AVOCADO, a standardized challenge framework that provides clear structural divisions: separating the concept and instantiation layers of challenges in streaming process mining for algorithm evaluation. The AVOCADO evaluates algorithms on streaming-specific metrics like accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Processing Latency, and robustness. This initiative seeks to foster innovation and community-driven discussions to advance the field of streaming process mining. We present this framework as a foundation and invite the community to contribute to its evolution by suggesting new challenges, such as integrating metrics for system throughput and memory consumption, and expanding the scope to address real-world stream complexities like out-of-order event arrival.

cs.DB↗

arXiv2025

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML

Edge computing offers significant advantages for realtime data processing tasks, such as object recognition, by reducing network latency and bandwidth usage. However, edge environments are susceptible to various types of fault. A remediator is an automated software component designed to adjust the configuration parameters of a software service dynamically. Its primary function is to maintain the services operational state within predefined Service Level Objectives by applying corrective actions in response to deviations from these objectives. Remediators can be implemented based on the Kubernetes container orchestration tool by implementing remediation strategies such as rescheduling or adjusting application parameters. However, currently, there is no method to compare these remediation strategies fairly. This paper introduces Ecoscape, a comprehensive benchmark designed to evaluate the performance of remediation strategies in fault-prone environments. Using Chaos Engineering techniques, Ecoscape simulates realistic fault scenarios and provides a quantifiable score to assess the efficacy of different remediation approaches. In addition, it is configurable to support domain-specific Service Level Objectives. We demonstrate the capabilities of Ecoscape in edge machine learning inference, offering a clear framework to optimize fault tolerance in these systems without needing a physical edge testbed.

cs.PF↗

arXiv2025

Process Mining on Distributed Data Sources

Major domains such as logistics, healthcare, and smart cities increasingly rely on sensor technologies and distributed infrastructures to monitor complex processes in real time. These developments are transforming the data landscape from discrete, structured records stored in centralized systems to continuous, fine-grained, and heterogeneous event streams collected across distributed environments. As a result, traditional process mining techniques, which assume centralized event logs from enterprise systems, are no longer sufficient. In this paper, we discuss the conceptual and methodological foundations for this emerging field. We identify three key shifts: from offline to online analysis, from centralized to distributed computing, and from event logs to sensor data. These shifts challenge traditional assumptions about process data and call for new approaches that integrate infrastructure, data, and user perspectives. To this end, we define a research agenda that addresses six interconnected fields, each spanning multiple system dimensions. We advocate a principled methodology grounded in algorithm engineering, combining formal modeling with empirical evaluation. This approach enables the development of scalable, privacy-aware, and user-centric process mining techniques suitable for distributed environments. Our synthesis provides a roadmap for advancing process mining beyond its classical setting, toward a more responsive and decentralized paradigm of process intelligence.

cs.ET↗

arXiv2025

DPM-Bench: Benchmark for Distributed Process Mining Algorithms on Cyber-Physical Systems

Process Mining is established in research and industry systems to analyze and optimize processes based on event data from information systems. Within this work, we accomodate process mining techniques to Cyber-Physical Systems. To capture the distributed and heterogeneous characteristics of data, computational resources, and network communication in CPS, the todays process mining algorithms and techniques must be augmented. Specifically, there is a need for new Distributed Process Mining algorithms that enable computations to be performed directly on edge resources, eliminating the need for moving all data to central cloud systems. This paper introduces the DPM-Bench benchmark for comparing such Distributed Process Mining algorithms. DPM-Bench is used to compare algorithms deployed in different computational topologies. The results enable information system engineers to assess whether the existing infrastructure is sufficient to perform distributed process mining, or to identify required improvements in algorithms and hardware. We present and discuss an experimental evaluation with DPM-Bench.

cs.SE↗