Device Data Ingestion for Industrial Big Data Platforms with a Case Study †

Despite having played a significant role in the Industry 4.0 era, the Internet of Things is currently faced with the challenge of how to ingest large-scale heterogeneous and multi-type device data. In response to this problem we present a heterogeneous device data ingestion model for an industrial b...

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Detalles Bibliográficos
Autores principales: Ji, Cun, Shao, Qingshi, Sun, Jiao, Liu, Shijun, Pan, Li, Wu, Lei, Yang, Chenglei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813854/
https://www.ncbi.nlm.nih.gov/pubmed/26927121
http://dx.doi.org/10.3390/s16030279
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author Ji, Cun
Shao, Qingshi
Sun, Jiao
Liu, Shijun
Pan, Li
Wu, Lei
Yang, Chenglei
author_facet Ji, Cun
Shao, Qingshi
Sun, Jiao
Liu, Shijun
Pan, Li
Wu, Lei
Yang, Chenglei
author_sort Ji, Cun
collection PubMed
description Despite having played a significant role in the Industry 4.0 era, the Internet of Things is currently faced with the challenge of how to ingest large-scale heterogeneous and multi-type device data. In response to this problem we present a heterogeneous device data ingestion model for an industrial big data platform. The model includes device templates and four strategies for data synchronization, data slicing, data splitting and data indexing, respectively. We can ingest device data from multiple sources with this heterogeneous device data ingestion model, which has been verified on our industrial big data platform. In addition, we present a case study on device data-based scenario analysis of industrial big data.
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spelling pubmed-48138542016-04-06 Device Data Ingestion for Industrial Big Data Platforms with a Case Study † Ji, Cun Shao, Qingshi Sun, Jiao Liu, Shijun Pan, Li Wu, Lei Yang, Chenglei Sensors (Basel) Article Despite having played a significant role in the Industry 4.0 era, the Internet of Things is currently faced with the challenge of how to ingest large-scale heterogeneous and multi-type device data. In response to this problem we present a heterogeneous device data ingestion model for an industrial big data platform. The model includes device templates and four strategies for data synchronization, data slicing, data splitting and data indexing, respectively. We can ingest device data from multiple sources with this heterogeneous device data ingestion model, which has been verified on our industrial big data platform. In addition, we present a case study on device data-based scenario analysis of industrial big data. MDPI 2016-02-26 /pmc/articles/PMC4813854/ /pubmed/26927121 http://dx.doi.org/10.3390/s16030279 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ji, Cun
Shao, Qingshi
Sun, Jiao
Liu, Shijun
Pan, Li
Wu, Lei
Yang, Chenglei
Device Data Ingestion for Industrial Big Data Platforms with a Case Study †
title Device Data Ingestion for Industrial Big Data Platforms with a Case Study †
title_full Device Data Ingestion for Industrial Big Data Platforms with a Case Study †
title_fullStr Device Data Ingestion for Industrial Big Data Platforms with a Case Study †
title_full_unstemmed Device Data Ingestion for Industrial Big Data Platforms with a Case Study †
title_short Device Data Ingestion for Industrial Big Data Platforms with a Case Study †
title_sort device data ingestion for industrial big data platforms with a case study †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813854/
https://www.ncbi.nlm.nih.gov/pubmed/26927121
http://dx.doi.org/10.3390/s16030279
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