An ontology-based monitoring system for multi-source environmental observations

Masmoudi, Maroua; Lamine, Sana Ben Abdallah Ben; Zghal, Hajer Baazaoui; Karray, Mohamed Hedi; Archimède, Bernard

doi:10.1016/j.procs.2018.08.076

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…In addition, [23] proposes the Meteorological Disaster Ontology (MDO), which can systematically describe the components of weather hazards as an ontology. As an extension from the existing ontologies, [24] proposes a new ontology that extends the Basic Formal Ontology (BFO).…”

Section: Semantic Web and Sensorsmentioning

confidence: 99%

RW-QAnswer: an assisting system for intelligent environments using semantic technology

Shimoda

Diefenbach²,

Singh

et al. 2020

J Reliable Intell Environ

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We have seen rapid growth of Internet of Things (IoT) paradigm. Challenges of IoT include the need to obtain comprehensive environmental information using multiple and different types of sensors as well as the need to reduce the amount of bandwidth used by a large number of sensors. Especially when image sensors are used, a significant amount of bandwidth is used for sending images. In addition, the use of IoT devices need not be limited to experts who are good at handling sensors. For example, there is the home IoT system which is expected to be used at home by several users. Thus, it is necessary to have an interface which is easy to handle, even for people who are not good at handling data and sensors. To solve these problems, we use Resource Description Framework (RDF) used in the Semantic Web field as metadata of sensor data for comprehensive environmental information acquisition. Then, by linking with an existing RDF search system called QAnswer, which uses natural language, we create a system that enables sensor data search using natural language. Thus, we design an intelligent system which enables users to interact with sensors using natural language. By combining an RDF database and a server which controls the flow of messages, we then investigate the trade-off between the response time to a user's request and the amount of bandwidth usage by messages. Our results show that in a sensor network using RDF, it is possible to reduce the amount of communication traffic by optimally transferring RDF and sensor data only on arrival of a request and this can be done without much increase in the communication latency.

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Section: Semantic Web and Sensorsmentioning

confidence: 99%

RW-QAnswer: an assisting system for intelligent environments using semantic technology

Shimoda

Diefenbach²,

Singh

et al. 2020

J Reliable Intell Environ

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“…1) The Modular Environmental Monitoring Ontology (MEMOn): Several works proposed ontologies for the environmental monitoring domain, discussed in [18]. To address their problems, we propose MEMOn, a modular ontology for the environmental monitoring field, based on the upper level ontology Basic Formal Ontology (BFO) [2] and other existing ontologies such as the Common Core Ontologies (CCO), the Semantic Sensor Netowork ontology (SSN) and the ENVironment Ontology (ENVO).…”

Section: Semantic Layermentioning

confidence: 99%

PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

Masmoudi

Taktak

Lamine³

et al. 2018

2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA)

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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).

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WDA: A Domain-Aware Database Schema Analysis for Improving OBDA-Based Event Log Extractions

Ponniah

Agarwal

2022

Advanced Data Mining and Applications

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An ontology-based monitoring system for multi-source environmental observations

Cited by 8 publications

References 11 publications

RW-QAnswer: an assisting system for intelligent environments using semantic technology

RW-QAnswer: an assisting system for intelligent environments using semantic technology

PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

WDA: A Domain-Aware Database Schema Analysis for Improving OBDA-Based Event Log Extractions

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