Cross-Correlation as Tool to Determine the Similarity of Series of Measurements for Big-Data Analysis Tasks

Hilbrich, Marcus; Müller-Pfefferkorn, R.

doi:10.1007/978-3-319-28430-9_20

Cited by 3 publications

(6 citation statements)

References 21 publications

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“…AMon is used [13] for visualizing and analyzing the monitoring data. AMon can be used as a desktop application or can be provided by a server and accessed via a web browser.…”

Section: Fundamentals Of Job-centric Monitoringmentioning

confidence: 99%

“…A previously described analysis method [13] based on similarity functions requires an alignment with complexity up to O(n 5 ). For calculating statistical functions, we need to read the series of measurements once and perform some calculations per measurement point (e.g., cooperation to find the minimum or summing up to calculate the mean).…”

Section: Statistical Functionsmentioning

confidence: 99%

“…The most-promising methods we found and developed further are based on similarity comparison. The first steps in using the cross correlation function to compare job-centric monitoring is shown by [13]. Further developments have already been done; so besides the local and global normalization and linear time adaption explained by [13], an automatic adoption to time drifts based on an optimization strategy using educated guessing was developed.…”

Section: Comparison To Similarity Functionsmentioning

confidence: 99%

“…Therefore, we started by studying related work [14], where we identified a set of analysis techniques like genetic algorithms [6,7,17,29], machine learning [2,[19][20][21], sequence comparison [1,9,24,25], intrusion detection [5,26,27], and statistic of events [4,18,28]. The most promising technique is a similarity comparison [8,13]. However, this method is complex and computing-intensive.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Series of Measurements From Job-Centric Monitoring by Statistical Functions

Hilbrich

Frank

2017

csci

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The rising number of executed programs (jobs) enabled by the growing amount of available resources from Clouds, Grids, and HPC (for example) has resulted in an enormous number of jobs. Nowadays, most of the executed jobs are mainly unobserved, so unusual behavior, non-optimal resource usage, and silent faults are not systematically searched and analyzed. Job-centric monitoring enables permanent job observation and, thus, enables the analysis of monitoring data.In this paper, we show how statistic functions can be used to analyze job-centric monitoring data and how the methods compare to more-complex analysis methods. Additionally, we present the usefulness of job-centric monitoring based on practical experiences.

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“…AMon is used [13] for visualizing and analyzing the monitoring data. AMon can be used as a desktop application or can be provided by a server and accessed via a web browser.…”

Section: Fundamentals Of Job-centric Monitoringmentioning

confidence: 99%

Section: Statistical Functionsmentioning

confidence: 99%

Section: Comparison To Similarity Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Series of Measurements From Job-Centric Monitoring by Statistical Functions

Hilbrich

Frank

2017

csci

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“…Te normalized cross-correlation (NCC) [34], which calculates the inner product between two diferent signals, is a widely used area-based similarity recognition algorithm [35]. It has been widely used in electrocardiogram (ECG) signal processing [36], audio/ speech signal processing [37][38][39], and big data analysis [40]. Klausen and Robbersmyr [41] applied the NCC to calculate the cross-correlation between a whitened vibration signal and its envelope to analyze bearing faults.…”

Section: Introductionmentioning

confidence: 99%

Theoretical and Experimental Study on Homologous Acoustic Emission Signal Recognition Based on Synchrosqueezed Wavelet Transform Coherence

Wang,

Huo,

Liu

et al. 2023

Structural Control and Health Monitoring

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The acoustic emission (AE) technique has been widely investigated for its ability to locate damage in structures. However, the selection of the arrival point of AE signals and the existence of nonhomologous AE signals can significantly affect the location accuracy of damages. The synchrosqueezed wavelet transform (SWT) was used in our previous research to pick the accurate arrival point, but the existence of the nonhomologous signals was neglected in the picking process. To address this limitation, the synchrosqueezed wavelet transform coherence (SWTC) method is proposed to improve the accuracy by recognizing homologous signals and suppressing the spectral leakage in this paper. Compared with the wavelet transform coherence (WTC) method previously used, the SWTC method using the squeezing wavelet coefficients obtained by the SWT can constitute a more explicit coherence graph of AE signals. This clear coherence graph can help reduce the effect of subjective factors in observing the coherence and improve the recognition accuracy of homologous signals. The effectiveness of the proposed method is experimentally verified on a steel pipe and a concrete beam. The results demonstrate that the SWTC accurately identifies homologous AE signals and effectively improves the localization accuracy across different signal densities, localization distances, and materials.

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Time-Aligned Similarity Calculations for Job-Centric Monitoring

Hilbrich¹,

Frank²

2017

2017 International Conference on Green Informatics (ICGI)

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Cross-Correlation as Tool to Determine the Similarity of Series of Measurements for Big-Data Analysis Tasks

Cited by 3 publications

References 21 publications

Analysis of Series of Measurements From Job-Centric Monitoring by Statistical Functions

Analysis of Series of Measurements From Job-Centric Monitoring by Statistical Functions

Theoretical and Experimental Study on Homologous Acoustic Emission Signal Recognition Based on Synchrosqueezed Wavelet Transform Coherence

Time-Aligned Similarity Calculations for Job-Centric Monitoring

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