Deterministic Time-Series Joins for Asynchronous High-Throughput Data Streams

Schranz, Christoph; Jeremias, Peter Michael

doi:10.1109/etfa46521.2020.9211958

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…The Nearest Advocate (NAd) algorithm, based on a zipper principle adapted from a stream-stream join algorithm [22], estimates the time delay between two event-based time-series data. The core concept involves calculating the distance between each event in one time-series and the nearest event (i.e., advocate) in another time-series for a given time offset φ .…”

Section: Algorithmmentioning

confidence: 99%

Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization

Schranz,

Mayr,

Bernhart

et al. 2024

EURASIP J. Adv. Signal Process.

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Estimating time delays in event-based time-series is a crucial task in signal processing as it affects the data quality and is a prerequisite for many subsequent analyses. In particular, data acquired from wearable devices often suffer from a low timestamp precision or clock drift. Current state-of-the-art methods such as Pearson Cross-Correlation are sensitive to typical data quality issues, e.g. misdetected events, and Dynamic Time Warping is computationally expensive. To overcome these limitations, we propose Nearest Advocate, a novel event-based time delay estimation method for multi-sensor time-series data synchronisation. We evaluate its performance using three independent datasets acquired from wearable sensor systems, demonstrating its superior precision, particularly for short, noisy time-series with missing events. Additionally, we introduce a sparse variant that balances precision and runtime. Finally, we demonstrate how Nearest Advocate can be used to solve the problem of linear as well as non-linear clock drifts. Thus, Nearest Advocate offers a promising opportunity for time delay estimation and post-hoc synchronization for challenging datasets across various applications.

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Section: Algorithmmentioning

confidence: 99%