Special Issue on Smart Data and Semantics in a Sensor World

Ilarri, S. (Universidad de Zaragoza) ; Po, L.R. ; Trillo-Lado, R. (Universidad de Zaragoza)

Resumen: 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...

Idioma: Inglés

DOI: 10.3390/app10186355

Año: 2020

Publicado en: APPLIED SCIENCES-BASEL 10, 18 (2020), 6355 [4 pp]
ISSN: 2076-3417

Factor impacto JCR: 2.679 (2020)

Categ. JCR: PHYSICS, APPLIED rank: 73 / 160 = 0.456 (2020) - Q2 - T2
Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 38 / 91 = 0.418 (2020) - Q2 - T2
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 101 / 178 = 0.567 (2020) - Q3 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 201 / 333 = 0.604 (2020) - Q3 - T2

Factor impacto SCIMAGO: 0.435 - Computer Science Applications (Q2) - Engineering (miscellaneous) (Q2) - Process Chemistry and Technology (Q2) - Instrumentation (Q2) - Materials Science (miscellaneous) (Q2) - Fluid Flow and Transfer Processes (Q2)

Financiación: info:eu-repo/grantAgreement/ES/AEI-FEDER/TIN2016-78011-C4-3-R
Financiación: info:eu-repo/grantAgreement/EC/CEF Telecom/2017-EU-IA-0167/EU/Understanding Traffic Flows to Improve Air quality/TRAFAIR
Financiación: info:eu-repo/grantAgreement/ES/DGA/T64-20R

Tipo y forma: (Versión definitiva)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)