eprintid: 17712
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/77/12
datestamp: 2023-12-19 03:24:03
lastmod: 2023-12-19 03:24:03
status_changed: 2023-12-19 03:08:32
type: conference_item
metadata_visibility: show
creators_name: Danyaro, K.U.
creators_name: Liew, M.S.
title: Semantic Web for Meteorological and Oceanographic Data
ispublished: pub
keywords: Benchmarking; Gas industry; Metadata; Ontology; Query processing; Resource Description Framework (RDF), D2RQ; Data and information; Meteorological and oceanographic data; Oil and gas; Oil and gas data integration; Performance; Resources description frameworks; Science research; Semantic-Web; Semantic-Web techniques, Data integration
note: cited By 1; Conference of 2nd International Conference on Computing and Information Technology, ICCIT 2022 ; Conference Date: 25 January 2022 Through 27 January 2022; Conference Code:177361
abstract: Data science research is now transforming the world of data and information technology into a new crucial paradigm. Many academic researchers have gotten interested in this matter. The goal of this study is to provide a semantic web technique for the oil and gas industry that includes an architecture for data integration. Semantic web technologies allow the process of extending the web in which information can be exchanged and shared in a meaningful way. However, with the amount of data increasing every day, there is a need for a semantic web system in all data industries. The modelling of ontologies and relational databases (RDB) into a resource description framework (RDF) is an important part of constructing the semantic web, which this study has adopted. Oil and gas data, in particular, meteorological and oceanographic (MetOcean) data, was used in this study. Applications such as Database to RDF Query (D2RQ), protégé, and web ontology language (OWL) reasoners were utilised for setup and performance in putting the findings into practice. The Berlin SPARQL Benchmark (BSBM) was used to analyze the performance of MetOceanSemWeb in order to provide a complete assessment of the findings. MetOceanSemWeb has therefore proven to be sufficient for adoption in the MetOcean sector. Furthermore, a scalability on D2RQ system was discovered due to the performance comparison. © 2022 IEEE.
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126808236&doi=10.1109%2fICCIT52419.2022.9711621&partnerID=40&md5=6f86edbcab3ecaa287ad65e8463fbf2a
id_number: 10.1109/ICCIT52419.2022.9711621
full_text_status: none
publication: Proceedings of 2022 2nd International Conference on Computing and Information Technology, ICCIT 2022
pagerange: 304-309
refereed: TRUE
isbn: 9781665436052
citation:   Danyaro, K.U. and Liew, M.S.  (2022) Semantic Web for Meteorological and Oceanographic Data.  In: UNSPECIFIED.