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DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added...

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Detalles Bibliográficos
Autores principales: Perez-Castillo, Ricardo, Carretero, Ana G., Caballero, Ismael, Rodriguez, Moises, Piattini, Mario, Mate, Alejandro, Kim, Sunho, Lee, Dongwoo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165119/
https://www.ncbi.nlm.nih.gov/pubmed/30223516
http://dx.doi.org/10.3390/s18093105
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author Perez-Castillo, Ricardo
Carretero, Ana G.
Caballero, Ismael
Rodriguez, Moises
Piattini, Mario
Mate, Alejandro
Kim, Sunho
Lee, Dongwoo
author_facet Perez-Castillo, Ricardo
Carretero, Ana G.
Caballero, Ismael
Rodriguez, Moises
Piattini, Mario
Mate, Alejandro
Kim, Sunho
Lee, Dongwoo
author_sort Perez-Castillo, Ricardo
collection PubMed
description The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers and vendors should align their data quality management mechanisms and artefacts with well-known best practices and standards, as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. The methodology consists of four steps according to the Plan-Do-Check-Act cycle by Deming.
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spelling pubmed-61651192018-10-10 DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data Perez-Castillo, Ricardo Carretero, Ana G. Caballero, Ismael Rodriguez, Moises Piattini, Mario Mate, Alejandro Kim, Sunho Lee, Dongwoo Sensors (Basel) Article The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers and vendors should align their data quality management mechanisms and artefacts with well-known best practices and standards, as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. The methodology consists of four steps according to the Plan-Do-Check-Act cycle by Deming. MDPI 2018-09-14 /pmc/articles/PMC6165119/ /pubmed/30223516 http://dx.doi.org/10.3390/s18093105 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Perez-Castillo, Ricardo
Carretero, Ana G.
Caballero, Ismael
Rodriguez, Moises
Piattini, Mario
Mate, Alejandro
Kim, Sunho
Lee, Dongwoo
DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data
title DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data
title_full DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data
title_fullStr DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data
title_full_unstemmed DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data
title_short DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data
title_sort daqua-mass: an iso 8000-61 based data quality management methodology for sensor data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165119/
https://www.ncbi.nlm.nih.gov/pubmed/30223516
http://dx.doi.org/10.3390/s18093105
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