000096147 001__ 96147 000096147 005__ 20210902121848.0 000096147 0247_ $$2doi$$a10.3390/app10186355 000096147 0248_ $$2sideral$$a120573 000096147 037__ $$aART-2020-120573 000096147 041__ $$aeng 000096147 100__ $$0(orcid)0000-0002-7073-219X$$aIlarri, S.$$uUniversidad de Zaragoza 000096147 245__ $$aSpecial Issue on Smart Data and Semantics in a Sensor World 000096147 260__ $$c2020 000096147 5060_ $$aAccess copy available to the general public$$fUnrestricted 000096147 5203_ $$aIntroduction 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... 000096147 536__ $$9info:eu-repo/grantAgreement/ES/AEI-FEDER/TIN2016-78011-C4-3-R$$9info:eu-repo/grantAgreement/EC/CEF Telecom/2017-EU-IA-0167/EU/Understanding Traffic Flows to Improve Air quality/TRAFAIR$$9info:eu-repo/grantAgreement/ES/DGA/T64-20R 000096147 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000096147 590__ $$a2.679$$b2020 000096147 591__ $$aPHYSICS, APPLIED$$b73 / 160 = 0.456$$c2020$$dQ2$$eT2 000096147 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b38 / 91 = 0.418$$c2020$$dQ2$$eT2 000096147 591__ $$aCHEMISTRY, MULTIDISCIPLINARY$$b101 / 178 = 0.567$$c2020$$dQ3$$eT2 000096147 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b201 / 333 = 0.604$$c2020$$dQ3$$eT2 000096147 592__ $$a0.435$$b2020 000096147 593__ $$aComputer Science Applications$$c2020$$dQ2 000096147 593__ $$aEngineering (miscellaneous)$$c2020$$dQ2 000096147 593__ $$aProcess Chemistry and Technology$$c2020$$dQ2 000096147 593__ $$aInstrumentation$$c2020$$dQ2 000096147 593__ $$aMaterials Science (miscellaneous)$$c2020$$dQ2 000096147 593__ $$aFluid Flow and Transfer Processes$$c2020$$dQ2 000096147 655_4 $$ainfo:eu-repo/semantics/other$$vinfo:eu-repo/semantics/publishedVersion 000096147 700__ $$aPo, L.R. 000096147 700__ $$0(orcid)0000-0001-6008-1138$$aTrillo-Lado, R.$$uUniversidad de Zaragoza 000096147 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000096147 773__ $$g10, 18 (2020), 6355 [4 pp]$$pAppl. sci.$$tAPPLIED SCIENCES-BASEL$$x2076-3417 000096147 8564_ $$s127816$$uhttps://zaguan.unizar.es/record/96147/files/texto_completo.pdf$$yVersión publicada 000096147 8564_ $$s524690$$uhttps://zaguan.unizar.es/record/96147/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000096147 909CO $$ooai:zaguan.unizar.es:96147$$particulos$$pdriver 000096147 951__ $$a2021-09-02-10:28:03 000096147 980__ $$aARTICLE