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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MDPI AG
2018-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/9/3105 |
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| author | Ricardo Perez-Castillo Ana G. Carretero Ismael Caballero Moises Rodriguez Mario Piattini Alejandro Mate Sunho Kim Dongwoo Lee |
| author_facet | Ricardo Perez-Castillo Ana G. Carretero Ismael Caballero Moises Rodriguez Mario Piattini Alejandro Mate Sunho Kim Dongwoo Lee |
| author_sort | Ricardo Perez-Castillo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T13:07:39Z |
| format | Article |
| id | doaj.art-edd2452f6ad246999c5412d69b3ff6d4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:07:39Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-edd2452f6ad246999c5412d69b3ff6d42022-12-22T04:22:41ZengMDPI AGSensors1424-82202018-09-01189310510.3390/s18093105s18093105DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor DataRicardo Perez-Castillo0Ana G. Carretero1Ismael Caballero2Moises Rodriguez3Mario Piattini4Alejandro Mate5Sunho Kim6Dongwoo Lee7Information Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, SpainInformation Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, SpainInformation Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, SpainInformation Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, SpainInformation Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, SpainLucentia Lab, University of Alicante, 03690 San Vicente del Raspeig, Alicante, SpainDepartment of Industrial & Management Engineering, Myongji University, Seoul 449-728, KoreaGTOne, Seoul 07299, KoreaThe 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.http://www.mdpi.com/1424-8220/18/9/3105data qualitydata quality management processesISO 8000-61data quality in sensorsInternet-of-ThingsIoTSmart, Connected ProductsSCPs |
| spellingShingle | Ricardo Perez-Castillo Ana G. Carretero Ismael Caballero Moises Rodriguez Mario Piattini Alejandro Mate Sunho Kim Dongwoo Lee DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data Sensors data quality data quality management processes ISO 8000-61 data quality in sensors Internet-of-Things IoT Smart, Connected Products SCPs |
| 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 | data quality data quality management processes ISO 8000-61 data quality in sensors Internet-of-Things IoT Smart, Connected Products SCPs |
| url | http://www.mdpi.com/1424-8220/18/9/3105 |
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