A Semantic Interoperability Approach for Heterogeneous Meteorology Big IoT Data

Bouaicha, Souad; Ghemmaz, Wafa

doi:10.1007/978-3-031-25344-7_20

Cited by 1 publication

(1 citation statement)

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“…This can result in better data exchange and more efficient data use, reducing the need for manual data entry and the risk of errors. For instance, Bouaicha and Ghemmaz [4] proposes a method for integrating and harmonizing data from various IoT sources in order to facilitate the exchange and processing of meteorological data. The approach makes use of semantic technologies to provide a common understanding of the data and to solve any semantic inconsistencies that may exist between various IoT sources.…”

Section: Introductionmentioning

confidence: 99%

Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data

Garcia

Peyman

Piè

et al. 2023

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In this paper, we present a novel framework for enriching time series data in smart cities by supplementing it with information from external sources via semantic data enrichment. Our methodology effectively merges multiple data sources into a uniform time series, while addressing difficulties such as data quality, contextual information, and time lapses. We demonstrate the efficacy of our method through a case study in Barcelona, which permitted the use of advanced analysis methods such as windowed cross-correlation and peak picking. The resulting time series data can be used to determine traffic patterns and has potential uses in other smart city sectors, such as air quality, energy efficiency, and public safety. Interactive dashboards enable stakeholders to visualize and summarize key insights and patterns.

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Section: Introductionmentioning

confidence: 99%