This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream.The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of “basis” series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs.

Candelieri, A., Fedorov, S., Messina, E. (2019). Efficient kernel-based subsequence search for enabling health monitoring services in IoT-based home setting. SENSORS, 19(23) [10.3390/s19235192].

Efficient kernel-based subsequence search for enabling health monitoring services in IoT-based home setting

Candelieri A.
Primo
Membro del Collaboration Group
;
Fedorov S.
Secondo
Membro del Collaboration Group
;
Messina E.
Ultimo
Membro del Collaboration Group
2019

Abstract

This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream.The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of “basis” series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs.
Articolo in rivista - Articolo scientifico
Data stream analysis; Dynamic time warping; Kernel learning; Pattern query; Subsequence search;
English
2019
19
23
5192
open
Candelieri, A., Fedorov, S., Messina, E. (2019). Efficient kernel-based subsequence search for enabling health monitoring services in IoT-based home setting. SENSORS, 19(23) [10.3390/s19235192].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/263034
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