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Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications

The Internet of Things (IoT) has penetrating all things and objects around us giving them the ability to interact with the Internet, i.e., things become Smart Things (SThs). As a result, SThs produce massive real-time data (i.e., big IoT data). Smartness of IoT applications bases mainly on services...

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Autores principales: Younan, Mina, Houssein, Essam H., Elhoseny, Mohamed, Ali, Abd El-mageid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157125/
https://www.ncbi.nlm.nih.gov/pubmed/34084921
http://dx.doi.org/10.7717/peerj-cs.500
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author Younan, Mina
Houssein, Essam H.
Elhoseny, Mohamed
Ali, Abd El-mageid
author_facet Younan, Mina
Houssein, Essam H.
Elhoseny, Mohamed
Ali, Abd El-mageid
author_sort Younan, Mina
collection PubMed
description The Internet of Things (IoT) has penetrating all things and objects around us giving them the ability to interact with the Internet, i.e., things become Smart Things (SThs). As a result, SThs produce massive real-time data (i.e., big IoT data). Smartness of IoT applications bases mainly on services such as automatic control, events handling, and decision making. Consumers of the IoT services are not only human users, but also SThs. Consequently, the potential of IoT applications relies on supporting services such as searching, retrieving, mining, analyzing, and sharing real-time data. For enhancing search service in the IoT, our previous work presents a promising solution, called Cluster Representative (ClRe), for indexing similar SThs in IoT applications. ClRe algorithms could reduce similar indexing by O(K − 1), where K is number of Time Series (TS) in a cluster. Multiple extensions for ClRe algorithms were presented in another work for enhancing accuracy of indexed data. In this theme, this paper studies performance analysis of ClRe algorithms, proposes two novel execution methods: (a) Linear execution (LE) and (b) Pair-merge execution (PME), and studies sorting impact on TS execution for enhancing similarity rate for some ClRe extensions. The proposed execution methods are evaluated with real examples and proved using Szeged-weather dataset on ClRe 3.0 and its extensions; where they produce representatives with higher similarities compared to the other extensions. Evaluation results indicate that PME could improve performance of ClRe 3.0 by = 20.5%, ClRe 3.1 by = 17.7%, and ClRe 3.2 by = 6.4% in average.
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spelling pubmed-81571252021-06-02 Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications Younan, Mina Houssein, Essam H. Elhoseny, Mohamed Ali, Abd El-mageid PeerJ Comput Sci Algorithms and Analysis of Algorithms The Internet of Things (IoT) has penetrating all things and objects around us giving them the ability to interact with the Internet, i.e., things become Smart Things (SThs). As a result, SThs produce massive real-time data (i.e., big IoT data). Smartness of IoT applications bases mainly on services such as automatic control, events handling, and decision making. Consumers of the IoT services are not only human users, but also SThs. Consequently, the potential of IoT applications relies on supporting services such as searching, retrieving, mining, analyzing, and sharing real-time data. For enhancing search service in the IoT, our previous work presents a promising solution, called Cluster Representative (ClRe), for indexing similar SThs in IoT applications. ClRe algorithms could reduce similar indexing by O(K − 1), where K is number of Time Series (TS) in a cluster. Multiple extensions for ClRe algorithms were presented in another work for enhancing accuracy of indexed data. In this theme, this paper studies performance analysis of ClRe algorithms, proposes two novel execution methods: (a) Linear execution (LE) and (b) Pair-merge execution (PME), and studies sorting impact on TS execution for enhancing similarity rate for some ClRe extensions. The proposed execution methods are evaluated with real examples and proved using Szeged-weather dataset on ClRe 3.0 and its extensions; where they produce representatives with higher similarities compared to the other extensions. Evaluation results indicate that PME could improve performance of ClRe 3.0 by = 20.5%, ClRe 3.1 by = 17.7%, and ClRe 3.2 by = 6.4% in average. PeerJ Inc. 2021-05-20 /pmc/articles/PMC8157125/ /pubmed/34084921 http://dx.doi.org/10.7717/peerj-cs.500 Text en © 2021 Younan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Younan, Mina
Houssein, Essam H.
Elhoseny, Mohamed
Ali, Abd El-mageid
Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications
title Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications
title_full Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications
title_fullStr Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications
title_full_unstemmed Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications
title_short Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications
title_sort performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157125/
https://www.ncbi.nlm.nih.gov/pubmed/34084921
http://dx.doi.org/10.7717/peerj-cs.500
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