Matrix Profile-Based Approach to Industrial Sensor Data Analysis Inside RDBMS

Zymbler, Mikhail; Иванова, Е. В.

doi:10.3390/math9172146

Cited by 8 publications

(4 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The versatility and scalability of the MP technique make it a valuable tool for a wide range of time series data-mining tasks [14,15,27,31,32]. Its applications span diverse domains, such as website user data analysis [33], medical diagnostics [34], music analysis [35,36], and critical business applications [37,38]. In particular, the technique has found extensive use in domains such as finance, healthcare, weather, and sensor data analysis.…”

Section: Matrix Profile In Time Seriesmentioning

confidence: 99%

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Ghazvini,

Sànchez-Marrè,

Naderi

et al. 2024

Energies

View full text Add to dashboard Cite

Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures.

show abstract

Section: Matrix Profile In Time Seriesmentioning

confidence: 99%

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Ghazvini,

Sànchez-Marrè,

Naderi

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…Recently, Matrix Profile (MP) [27,30] is proposed as an efficient and effective data representation on subsequence level and support most major fundamental tasks for downstream applications in a broad range of domains applications [27,30,28]. Existing work such as [31] typically use the raw data to compute MP as the first, feature generation step, and then design algorithms or models based on the computed MP. None of the work considers the use of MP in the context of data sharing with a third party, nor the privacy issues raised by sensitive patterns.…”

Section: Related Workmentioning

confidence: 99%

PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series

Zhang¹,

Ding²,

Gao³

et al. 2023

Preprint

View full text Add to dashboard Cite

Recent rapid development of sensor technology has allowed massive fine-grained time series data to be collected and set foundation for the development of data-driven services and applications. During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining tasks based on the need of data owner. The high resolution of time series data brings new challenges in protecting privacy. On one hand, meaningful information in high-resolution time series shifts from concrete point values to local shape-based segments. On the other hand, numerous research have found that long shape-based patterns could contain more sensitive information and may potentially be extracted and misused by a malicious third party. However, the privacy issue for time series patterns is surprisingly seldom explored in privacy-preserving literature. In this work, we consider a new privacy preserving problem: preventing malicious inference on long shape-based patterns while preserving short segment information for the utility task performance. To mitigate the challenge, we investigate an alternative approach by sharing Matrix Profile (MP), which is a nonlinear transformation of original data and a versatile data structure that supports many data mining tasks. We found that while MP can prevent the concrete shape leakage, the canonical correlation in MP index can still reveal the location of sensitive long pattern information. Based on this observation, we design two attacks named Location Attack and Entropy Attack to extract the pattern location from MP. To further protect MP from these two attacks, we propose a Privacy-Aware Matrix Profile (PMP) via perturbing the local correlation and breaking the canonical correlation in MP index vector. We evaluate our proposed PMP against baseline noise-adding methods through quantitative analysis and real-world case study to show the effectiveness of the proposed method. Our source code is available at https://github.com/lzhang18/PMP.

show abstract

“…In this study, we address the problem of parallelization of the MERLIN algorithm for the discovery of arbitrary length discords on a GPU, continuing our research on accelerating various time series mining tasks with parallel architectures and in-database time series analysis [9][10][11][12][13][14][15][16]. The article's contribution can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

High-Performance Time Series Anomaly Discovery on Graphics Processors

Zymbler

Kraeva

2023

Mathematics

Self Cite

View full text Add to dashboard Cite

Currently, discovering subsequence anomalies in time series remains one of the most topical research problems. A subsequence anomaly refers to successive points in time that are collectively abnormal, although each point is not necessarily an outlier. Among numerous approaches to discovering subsequence anomalies, the discord concept is considered one of the best. A time series discord is intuitively defined as a subsequence of a given length that is maximally far away from its non-overlapping nearest neighbor. Recently introduced, the MERLIN algorithm discovers time series discords of every possible length in a specified range, thereby eliminating the need to set even that sole parameter to discover discords in a time series. However, MERLIN is serial, and its parallelization could increase the performance of discord discovery. In this article, we introduce a novel parallelization scheme for GPUs called PALMAD, parallel arbitrary length MERLIN-based anomaly discovery. As opposed to its serial predecessor, PALMAD employs recurrent formulas we have derived to avoid redundant calculations, and advanced data structures for the efficient implementation of parallel processing. Experimental evaluation over real-world and synthetic time series shows that our algorithm outperforms parallel analogs. We also apply PALMAD to discover anomalies in a real-world time series, employing our proposed discord heatmap technique to illustrate the results.

show abstract

Matrix Profile-Based Approach to Industrial Sensor Data Analysis Inside RDBMS

Cited by 8 publications

References 51 publications

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series

High-Performance Time Series Anomaly Discovery on Graphics Processors

Contact Info

Product

Resources

About