Data curation in the Internet of Things: A decision model approach
Current Internet of Things (IoT) scenarios have to deal with many challenges especially when a large amount of heterogeneous data sources are integrated, that is, data curation. In this respect, the use of poor-quality data (i.e., data with problems) can produce terrible consequence from incorrect d...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/135483 |
| Acceso en línea: | https://hdl.handle.net/11441/135483 https://doi.org/10.1002/cmm4.1191 |
| Access Level: | acceso abierto |
| Palabra clave: | Big data pipeline Data curation Data quality Internet of Things Sensors |
| Sumario: | Current Internet of Things (IoT) scenarios have to deal with many challenges especially when a large amount of heterogeneous data sources are integrated, that is, data curation. In this respect, the use of poor-quality data (i.e., data with problems) can produce terrible consequence from incorrect decision-making to damaging the performance in the operations. Therefore, using data with an acceptable level of usability has become essential to achieve success. In this article, we propose an IoT-big data pipeline architecture that enables data acqui sition and data curation in any IoT context. We have customized the pipeline by including the DMN4DQ approach to enable us the measuring and evaluat ing data quality in the data produced by IoT sensors. Further, we have chosen a real dataset from sensors in an agricultural IoT context and we have defined a decision model to enable us the automatic measuring and assessing of the data quality with regard to the usability of the data in the context |
|---|