96147 20210902121848.0 doi 10.3390/app10186355 sideral 120573 ART-2020-120573 eng Ilarri, S. Universidad de Zaragoza (orcid)0000-0002-7073-219X Special Issue on Smart Data and Semantics in a Sensor World 2020 Access copy available to the general public Unrestricted Introduction Since its first inception in 2001, the application of the Semantic Web [1, 2] has carried out an extensive use of ontologies [3–5], reasoning, and semantics in diverse fields, such as Information Integration, Software Engineering, Bioinformatics, eGovernment, eHealth, and social networks. This widespread use of ontologies has led to an incredible advance in the development of techniques to manipulate, share, reuse, and integrate information across heterogeneous data sources. In recent years, the growth of the IoT (Internet of Things) required to face the challenges of “Big Data” [6–10]. The cost of sensors is decreasing, while their use is expanding. Moreover, the use of multiple personal smart devices is an emerging trend and all of them can embed sensors to monitor the surrounding environment. Therefore, the number of available sensors is exploding. On the one hand, the flows of sensor data are massive and continuous, and the data could be obtained in real time or with a delay of just a few seconds. Then, the volume of sensor data is increasing continuously every day. On the other hand, the variety of data being generated is also increasing, due to plenty of different devices and different measures to record. There are many kinds of structured and unstructured sensor data in diverse formats. Moreover, data veracity, which is the degree of accuracy or truthfulness of a data set, is an important aspect to consider. In the context of sensor data, it represents the trustworthiness of the data source and the processing of data. The need for more accurate and reliable data was always declared, but often overlooked for the sake of larger and cheaper... info:eu-repo/grantAgreement/ES/AEI-FEDER/TIN2016-78011-C4-3-R info:eu-repo/grantAgreement/EC/CEF Telecom/2017-EU-IA-0167/EU/Understanding Traffic Flows to Improve Air quality/TRAFAIR info:eu-repo/grantAgreement/ES/DGA/T64-20R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.679 2020 PHYSICS, APPLIED 73 / 160 = 0.456 2020 Q2 T2 ENGINEERING, MULTIDISCIPLINARY 38 / 91 = 0.418 2020 Q2 T2 CHEMISTRY, MULTIDISCIPLINARY 101 / 178 = 0.567 2020 Q3 T2 MATERIALS SCIENCE, MULTIDISCIPLINARY 201 / 333 = 0.604 2020 Q3 T2 0.435 2020 Computer Science Applications 2020 Q2 Engineering (miscellaneous) 2020 Q2 Process Chemistry and Technology 2020 Q2 Instrumentation 2020 Q2 Materials Science (miscellaneous) 2020 Q2 Fluid Flow and Transfer Processes 2020 Q2 info:eu-repo/semantics/other info:eu-repo/semantics/publishedVersion Po, L.R. Trillo-Lado, R. Universidad de Zaragoza (orcid)0000-0001-6008-1138 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 10, 18 (2020), 6355 [4 pp] Appl. sci. APPLIED SCIENCES-BASEL 2076-3417 127816 http://zaguan.unizar.es/record/96147/files/texto_completo.pdf Versión publicada 524690 http://zaguan.unizar.es/record/96147/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:96147 articulos driver 2021-09-02-10:28:03 ARTICLE