PREDICAT: a semantic service-oriented platform for data interoperability and linking in earth observation and disaster prediction

The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service-oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS)

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

International audienc

Knowledge-Driven Automatic Solution for Service Composition based on ELECTRE III and Quality-Aware Service Selection for the Wildfire Prediction

Wildfire prediction gained much attention in recent years, but, it remains a computational challenge, since it requires the collection of several environmental parameters in real time, from diverse environmental data sources. Moreover, since the globalization of environmental Web Services accessing the environmental data sources, services can be functionally similar, but with different qualities of services. Therefore, environmental data sources compete to provide these functionally similar services with different levels of qualities related to the services (QoS) and also to the data sources and their related data (QoDS). Furthermore, automating and dynamically composing services, while taking prior domain knowledge into account remains an ongoing challenge, since the service composition process is data-driven in the context of wildfire prediction. Subsequently, we present an approach to fulfill the requirement of ranking and selecting the optimal services based on the data received from previous services calls and their related quality dimensions. Our contribution is an automatic, knowledge-driven approach that relies on the ELECTRE III MCDM (Multi-Criteria Decision Making) method and on quality-aware service selection, to automatically and dynamically compose services for the purpose of predicting wildfire catastrophe and triggering alerts

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

International audienc

A knowledge-driven service composition framework for wildfire prediction

International audienceWildfire prediction has drawn a lot of researchers’ interest, but still presents a computational difficulty since it necessitates real-time data collected from several distributed data sources. Furthermore, because environmental Web services have, now, access to a wider range of environmental data sources, services might be functionally similar but of varying quality. In this paper, we propose a knowledge-driven framework for service composition that is based on a layered architecture. Based on these layers, the proposed framework aims to select the optimal service instances participating in a service composition schema, through a modular ontology to infer the quality of data sources (QoDS) and an outranking approach. Moreover, it aims to executing the service composition schema at runtime by dynamically readjusting both the service composition schema and the service instances via a machine learning-based service composition approach. The conducted experiments showed that the proposed framework enables (i) a reasonable reasoning time for assessing the data sources’ quality, (ii) a decrease in the ELECTRE III MCDM method’s execution time achieved by combining the skyline and α-dominance methods, (iii) dynamic generation of the most relevant service composition schema with the appropriate wildfire risk classes, and (iv) a high prediction accuracy using our proposed outranking approach compared to the randomly selected services

Design of Bioinspired Emulsified Composite European Eel Gelatin and Protein Isolate-Based Food Packaging Film: Thermal, Microstructural, Mechanical, and Biological Features

International audienceThe study focused on the elaboration and the characterization of blend biofilms based on European eel skin gelatin (ESG) and protein isolate (EPI) and the assessment of European oil (EO) incorporation effect on their properties. Data displayed that the incorporation of EPI and EO to the gelatin formulation decreased the lightness and yellowness of composite and emulsified films, respectively, compared to ESG film. Moreover, ESG films exhibited improved mechanical properties than EPI films. FTIR analysis, all incorporated films with EO at the ratio 1:4 (oil/polymer) revealed similar characteristic bands as in free-oil films. Further, the SEM images of 100% ESG and 100% EPI films showed a smooth and homogenous structure, whereas the cross-section of blend film (at a ratio 50:50) displayed a rougher microstructure. In addition, emulsified film ESG100 revealed a smooth and homogeneous microstructure compared to that prepared using EPI/ESG 50/50 ratio. Furthermore, EPI or EO addition into the ESG matrix enhanced the blend films antioxidant activities