arXiv subjects

Patrick Rathje

Publications and source records attributed to Patrick Rathje.

5 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↗

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↗

arXiv2024

Precise Ranging: Modeling Bias and Variance of Double-Sided Two-Way Ranging with TDoA Extraction under Multipath and NLOS Effects

Location-based services such as autonomous vehicles, drones, and indoor positioning require precise and scalable distance estimates. The bias and variance of range estimators inherently influence the resulting localization quality. In this work, we revisit the well-established Double-Sided Two-Way-Ranging (DS-TWR) protocol and the extraction of timing differences (DS-TDoA) at devices overhearing DS-TWR. Under non-line-of-sight (NLOS) and multipath effects, we analytically derive their bias and variance. Our proposed model reveals that DS-TWR retains half the variance than anticipated while DS-TDoA comprises roughly a five-fold increase in variance. We conduct numerical simulations and experimental deployments using Ultra-Wideband (UWB) devices in a public testbed. Our results confirm the adequacy of our model, providing centimeter-accurate predictions based on the underlying timestamping noise with a median $R^2$ score of 77% (30% IQR). We find that both DS-TWR and DS-TDoA exhibit reduced variance when response times are symmetric. Our experimental results further show that double-sided variants exhibit less error and variance compared to Carrier Frequency Offset (CFO)-based single-sided methods.

eess.SP↗

arXiv2024

EdgeMiner: Distributed Process Mining at the Data Sources

Process mining is moving beyond mining traditional event logs and nowadays includes, for example, data sourced from sensors in the Internet of Things (IoT). The volume and velocity of data generated by such sensors makes it increasingly challenging to efficiently process the data by traditional process discovery algorithms, which operate on a centralized event log. This paper presents EdgeMiner, an algorithm for distributed process mining operating directly on sensor nodes on a stream of real-time event data. In contrast to centralized algorithms, EdgeMiner tracks each event and its predecessor and successor events directly on the sensor node where the event is sensed and recorded. As EdgeMiner aggregates direct successions on the individual nodes, the raw data does not need to be stored centrally, thus improving both scalability and privacy. We analytically and experimentally show the correctness of EdgeMiner. In addition, our evaluation results show that EdgeMiner determines predecessors for each event efficiently, reducing the communication overhead by up to 96% compared to querying all nodes. Further, we show that the number of queried nodes stabilizes after relatively few events, and batching predecessor queries in groups reduces the average queried nodes per event to less than 2.5%.

cs.DB↗

arXiv2024

Time Difference of Arrival Extraction from Two-Way Ranging

Two-Way Ranging enables the distance estimation between two active parties and allows time of flight measurements despite relative clock offset and drift. Limited by the number of messages, scalable solutions build on Time Difference of Arrival to infer timing information at passive listeners. However, the demand for accurate distance estimates dictates a tight bound on the time synchronization, thus limiting scalability to the localization of passive tags relative to static, synchronized anchors. This work describes the extraction of Time Difference of Arrival information from a Two-Way Ranging process, enabling the extraction of distance information on passive listeners and further allowing scalable tag localization without the need for static or synchronized anchors. The expected error is formally deducted. The extension allows the extraction of the timing difference despite relative clock offset and drift for the Double-Sided Two-Way Ranging and Single-Sided Two-Way Ranging with additional carrier frequency offset estimation.

eess.SP↗