Weather data publication on the LOD using SOSA/SSN ontology

Roussey, Catherine; Bernard, Stéphan; André, Géraldine; Boffety, D.

doi:10.3233/sw-200375

Cited by 13 publications

(14 citation statements)

References 6 publications

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“…The Scientific and Technical Information Department (DIPSO) of the French National Research Institute for Agriculture, Food and the Environment (INRAE) is heavily committed to developing semantic resources like ontologies and thesauri for data related uses within the organization. In particular, INRAE's laboratory of Clermont-Ferrand, Technologies and information Systems for agricultural systems (TSCF), works on the use of sensor data -represented as linked open data-to improve agricultural practices and decision support (Nguyen et al, 2020;Roussey et al, 2019). The two National Research Agency supported projects, D2KAB and FooSIN, 8 respectively headed by University of Montpellier and INRAE, are explicitly relying on Semantic Web technologies and SRs in their processes from transforming agrifood data to knowledge.…”

Section: Examples Of Outputs Adoptionmentioning

confidence: 99%

39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition

Caracciolo

Aubin

Jonquet

et al. 2020

CODATA

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In this paper, we report on the outputs and adoption of the Agrisemantics Working Group of the Research Data Alliance (RDA), consisting of a set of recommendations to facilitate the adoption of semantic technologies and methods for the purpose of data interoperability in the field of agriculture and nutrition. From 2016 to 2019, the group gathered researchers and practitioners at the crossing point between information technology and agricultural science, to study all aspects in the life cycle of semantic resources: conceptualization, edition, sharing, standardization, services, alignment, long term support. First, the working group realized a landscape study, a study of the uses of semantics in agrifood, then collected use cases for the exploitation of semantics resources -a generic term to encompass vocabularies, terminologies, thesauri, ontologies. The resulting requirements were synthesized into 39 "hints" for users and developers of semantic resources, and providers of semantic resource services. We believe adopting these recommendations will engage agrifood sciences in a necessary transition to leverage data production, sharing and reuse and the adoption of the FAIR data principles. The paper includes examples of adoption of those requirements, and a discussion of their contribution to the field of data science.

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Section: Examples Of Outputs Adoptionmentioning

confidence: 99%

39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition

Caracciolo

Aubin

Jonquet

et al. 2020

CODATA

Self Cite

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“…Then, a dedicated wrapper should be developed for each source to tag the data using the pre-defined scheme. In Listing 1 , an example of a semantized data sample adapted from [ 94 ] is shown. It uses previously mentioned vocabularies: SOSA ( ) to represent observations and CDT ( ) to describe measured values.…”

Section: Applications In Ubiquitous Sensingmentioning

confidence: 99%

“…Data generated in this way are therefore standardized and ready for further analysis. Depending on the needs, the semantic description can be more detailed, including, for example, sensor specifications and more metadata about the measurement site [ 94 ].…”

Section: Applications In Ubiquitous Sensingmentioning

confidence: 99%

Semantic Data Mining in Ubiquitous Sensing: A Survey

Nalepa

Bobek

Kutt

et al. 2021

Sensors

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Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome of the data mining process. With this, in general, the inclusion of domain knowledge leading towards semantic data mining approaches is an emerging and important research direction. This article aims to survey relevant works in these areas, focusing on semantic data mining approaches and methods, but also on selected applications of ubiquitous sensing in some of the most prominent current application areas. Here, we consider in particular: (1) environmental sensing; (2) ubiquitous sensing in industrial applications of artificial intelligence; and (3) social sensing relating to human interactions and the respective individual and collective behaviors. We discuss these in detail and conclude with a summary of this emerging field of research. In addition, we provide an outlook on future directions for semantic data mining in ubiquitous sensing contexts.

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“…In our experiments, we used a dataset provided by the INRAE Montoldre site [30,31], which is a large experimental farm located in Montoldre. This was later dedicated to the development and the experimentation of agri-environmental techniques.…”

Section: Dataset 421 Inrae Montoldre Sitementioning

confidence: 99%

Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

En-Nejjary

Pinet

Kang

2021

IJGI

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The size of spatial data is growing intensively due to the emergence of and the tremendous advances in technology such as sensors and the internet of things. Supporting high-performance queries on this large volume of data becomes essential in several data- and compute-intensive applications. Unfortunately, most of the existing methods and approaches are based on a traditional computing framework (uniprocessors) which makes them not scalable and not adequate to deal with large-scale data. In this work, we present a high-performance query for massive spatio–temporal data. The query consists of selecting fixed size raster subsequences, based on the average of their region of interest, from a spatio–temporal raster sequence satisfying a user threshold condition. In our paper, for the purpose of simplification, we consider that the region of interest is the entire raster and not only a subregion. Our aim is to speed up the execution using parallel primitives and pure CUDA. Furthermore, we propose a new method based on a sorting step to save computations and boost the speed of the query execution. The test results show that the proposed methods are faster and good performance is achieved even with large-scale rasters and data.

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Weather data publication on the LOD using SOSA/SSN ontology

Cited by 13 publications

References 6 publications

39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition

39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition

Semantic Data Mining in Ubiquitous Sensing: A Survey

Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

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