@inproceedings{240b67402ff6464c9a38a1e1dd36f827,
title = "Intelligent semantic query for manufacturing supply chain",
abstract = "Current trends in the manufacturing supply chain reveal a promising demand for the development and deployment of intelligent manufacturing systems, where the underlying intelligence of such systems allows enterprises to optimize their manufacturing sites. The aim of this paper is to develop an intelligent data query algorithm for a manufacturing supply chain through the use of semantic web technologies. This research demonstrates the benefits of using ontologies for a manufacturing data query by comparing three different search mechanisms based on multiple criteria. The best results are obtained by introducing a hybrid technique of having ontology and a data repository in place. This has been demonstrated by developing simulation software based on a real case study of a distributed manufacturing system.",
keywords = "Big data, Manufacturing data query, Ontology",
author = "Salman Saeidlou and Mozafar Saadat and {Amini Sharifi}, Ebrahim",
year = "2016",
doi = "10.3233/978-1-61499-668-2-419",
language = "English",
series = "Advances in Transdisciplinary Engineering",
publisher = "IOS Press BV",
pages = "419--424",
editor = "Goh, {Yee Mey} and Keith Case",
booktitle = "Advances in Manufacturing Technology XXX - Proceedings of the 14th International Conference on Manufacturing Research, ICMR2016 - Incorporating the 31st National Conference on Manufacturing Research",
note = "14th International Conference on Manufacturing Research, ICMR 2016 ; Conference date: 06-09-2016 Through 08-09-2016",
}