SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery

A common challenge in multimedia data understanding is the unsupervised discovery of recurring patterns, or motifs, in time series data. The discovery of motifs in uni-variate time series is a well studied problem and, while being a relatively new area of research, there are also several proposals f...

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Autores principales:	Silvestro R. Poccia, K. Selçuk Candan, Maria Luisa Sapino
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multi-variate time series motifs detection recurring pattern Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/cd46d70fe10047799b56921f16ec57a6
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spelling	oai:doaj.org-article:cd46d70fe10047799b56921f16ec57a62021-11-25T16:39:39ZSMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery10.3390/app1122108732076-3417https://doaj.org/article/cd46d70fe10047799b56921f16ec57a62021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10873https://doaj.org/toc/2076-3417A common challenge in multimedia data understanding is the unsupervised discovery of recurring patterns, or motifs, in time series data. The discovery of motifs in uni-variate time series is a well studied problem and, while being a relatively new area of research, there are also several proposals for multi-variate motif discovery. Unfortunately, motif search among multiple variates is an expensive process, as the potential number of sub-spaces in which a pattern can occur increases exponentially with the number of variates. Consequently, many multi-variate motif search algorithms make simplifying assumptions, such as searching for motifs across all variates individually, assuming that the motifs are of the same length, or that they occur on a fixed subset of variates. In this paper, we are interested in addressing a relatively broad form of multi-variate motif detection, which seeks frequently occurring patterns (of possibly differing lengths) in sub-spaces of a multi-variate time series. In particular, we aim to leverage contextual information to help select contextually salient patterns and identify the most frequent patterns among all. Based on these goals, we first introduce the <i>contextually salient multi-variate motif</i> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="monospace">CS</mi></semantics></math></inline-formula>-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="monospace">motif</mi></semantics></math></inline-formula>) <i>discovery</i> problem and then propose a <i>salient multi-variate motif (<i>SMM</i>)</i> algorithm that, unlike existing methods, is able to seek a broad range of patterns in multi-variate time series.Silvestro R. PocciaK. Selçuk CandanMaria Luisa SapinoMDPI AGarticlemulti-variate time seriesmotifs detectionrecurring patternTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10873, p 10873 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-variate time series motifs detection recurring pattern Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	multi-variate time series motifs detection recurring pattern Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Silvestro R. Poccia K. Selçuk Candan Maria Luisa Sapino SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
description	A common challenge in multimedia data understanding is the unsupervised discovery of recurring patterns, or motifs, in time series data. The discovery of motifs in uni-variate time series is a well studied problem and, while being a relatively new area of research, there are also several proposals for multi-variate motif discovery. Unfortunately, motif search among multiple variates is an expensive process, as the potential number of sub-spaces in which a pattern can occur increases exponentially with the number of variates. Consequently, many multi-variate motif search algorithms make simplifying assumptions, such as searching for motifs across all variates individually, assuming that the motifs are of the same length, or that they occur on a fixed subset of variates. In this paper, we are interested in addressing a relatively broad form of multi-variate motif detection, which seeks frequently occurring patterns (of possibly differing lengths) in sub-spaces of a multi-variate time series. In particular, we aim to leverage contextual information to help select contextually salient patterns and identify the most frequent patterns among all. Based on these goals, we first introduce the <i>contextually salient multi-variate motif</i> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="monospace">CS</mi></semantics></math></inline-formula>-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="monospace">motif</mi></semantics></math></inline-formula>) <i>discovery</i> problem and then propose a <i>salient multi-variate motif (<i>SMM</i>)</i> algorithm that, unlike existing methods, is able to seek a broad range of patterns in multi-variate time series.
format	article
author	Silvestro R. Poccia K. Selçuk Candan Maria Luisa Sapino
author_facet	Silvestro R. Poccia K. Selçuk Candan Maria Luisa Sapino
author_sort	Silvestro R. Poccia
title	SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
title_short	SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
title_full	SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
title_fullStr	SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
title_full_unstemmed	SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
title_sort	smm: leveraging metadata for contextually salient multi-variate motif discovery
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cd46d70fe10047799b56921f16ec57a6
work_keys_str_mv	AT silvestrorpoccia smmleveragingmetadataforcontextuallysalientmultivariatemotifdiscovery AT kselcukcandan smmleveragingmetadataforcontextuallysalientmultivariatemotifdiscovery AT marialuisasapino smmleveragingmetadataforcontextuallysalientmultivariatemotifdiscovery
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SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery

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