A Model-Driven Approach for Semantic Data-as-a-Service Generation

Taktak, Hela; Boukadi, Khouloud; Mrissa, Michaël; Ghedira-Guegan, Chirine; Gargouri, Faïez

doi:10.1109/wetice49692.2020.00055

Cited by 3 publications

(1 citation statement)

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“…Thus, all the abstract composition schema in the Service Composition layer are mapped to schema composition with concrete services. In fact, the service registry encompasses services descriptions expressed semantically as detailed in our previous work [6]. Thus, all the invoked services from the service registry, in the Service layer, are enhanced with EO linked-data.…”

Section: System Architecture For Predictionmentioning

confidence: 99%

Towards Knowledge-Driven Automatic Service Composition for Wildfire Prediction

Taktak

Boukadi²,

Ghedira-Guegan³

et al. 2021

Service-Oriented Computing – ICSOC 2020 Workshops

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Wildfire prediction from Earth Observation (EO) data has gained much attention in the past years, through the development of connected sensors and weather satellites. Nowadays, it is possible to extract knowledge from collected EO data and to learn from this knowledge without human intervention to trigger wildfire alerts. However, exploiting knowledge extracted from multiple EO data sources at run-time and predicting wildfire raise multiple challenges. One major challenge is to provide dynamic construction of service composition plans, according to the data obtained from sensors. In this paper, we present a knowledgedriven Machine Learning approach that relies on historical data related to wildfire observations to guide the collection of EO data and to automatically and dynamically compose services for triggering wildfire alerts.

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Section: System Architecture For Predictionmentioning

confidence: 99%