Analyzing particularities of sensor datasets for supporting data understanding and preparation

Vista sencilla de ítem
dc.contributor.authorNieto de Santos, Francisco Javier
dc.contributor.authorAguilera, Unai
dc.contributor.authorLópez de Ipiña González de Artaza, Diego
dc.date.accessioned2025-09-04T09:00:30Z
dc.date.available2025-09-04T09:00:30Z
dc.date.issued2021-09-10
dc.date.updated2025-09-04T09:00:30Z
dc.description.abstractData scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data). This work analyzes several aspects of the data generated by different sensor types to understand particularities in the data, linking them with existing data mining methodologies. Using data from different sources, this work analyzes how the type of sensor used and its measurement units have an important impact in basic statistics such as variance and mean, because of the statistical distributions of the datasets. The work also analyzes the behavior of outliers, how to detect them, and how they affect the equivalence of sensors, as equivalence is used in many solutions for identifying anomalies. Based on the previous results, the article presents guidance on how to deal with data coming from sensors, in order to understand the characteristics of sensor datasets, and proposes a parallelized implementation. Finally, the article shows that the proposed decision-making processes work well with a new type of sensor and that parallelizing with several cores enables calculations to be executed up to four times faster.en
dc.description.sponsorshipThis research was partially funded by the European Union Horizon 2020 research and innovation programme under grant agreements No 777549 (EUXDAT) and No 824115 (HiDALGO)en
dc.identifier.citationNieto, F. J., Aguilera, U., & López-De-ipiña, D. (2021). Analyzing particularities of sensor datasets for supporting data understanding and preparation. Sensors, 21(18). https://doi.org/10.3390/S21186063
dc.identifier.doi10.3390/S21186063
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3496
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2021 by the authors
dc.subject.otherAnomaly detection
dc.subject.otherData analysis parallelization
dc.subject.otherData understanding
dc.subject.otherInternet of things
dc.subject.otherSensor data analytics
dc.titleAnalyzing particularities of sensor datasets for supporting data understanding and preparationen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue18
oaire.citation.titleSensors
oaire.citation.volume21
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
nieto_analyzing_2021.pdf
Tamaño:
3.25 MB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos