Yassine Bel-Ghaddar, Abderrahmane Seriai, Ahlame Begdouri, C. Delenne, N. Chahinian, Mustapha Derras
{"title":"Combining model-driven engineering and sewerage networks: towards a generic representation","authors":"Yassine Bel-Ghaddar, Abderrahmane Seriai, Ahlame Begdouri, C. Delenne, N. Chahinian, Mustapha Derras","doi":"10.1109/CiSt49399.2021.9357171","DOIUrl":null,"url":null,"abstract":"Representing and processing digital data related to underground networks, particularly sewerage networks, is increasingly becoming a priority for the managers of these networks. Indeed, better representation would allow them, among others, to improve knowledge and to take the best decisions regarding these generally poorly identified infrastructures. The heterogeneity of data and the multiplicity of data models representing sewerage networks, often specific to each operator, as well as the imperfections associated with both the available data and those collected from different sources, generate complexity in terms of on-the-field interventions' efficiency. They also highlight the need for aggregation (unification), control and analysis. The main objective of our work is to merge multi-source data to obtain more precise and complete digital maps of sewerage networks. In this paper, we propose a generic data modelling for data fusion purposes taking into consideration the uncertainty aspects related to the collected data by allowing a confidence value for each data source and for each single data provided by a source.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"894 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CiSt49399.2021.9357171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Representing and processing digital data related to underground networks, particularly sewerage networks, is increasingly becoming a priority for the managers of these networks. Indeed, better representation would allow them, among others, to improve knowledge and to take the best decisions regarding these generally poorly identified infrastructures. The heterogeneity of data and the multiplicity of data models representing sewerage networks, often specific to each operator, as well as the imperfections associated with both the available data and those collected from different sources, generate complexity in terms of on-the-field interventions' efficiency. They also highlight the need for aggregation (unification), control and analysis. The main objective of our work is to merge multi-source data to obtain more precise and complete digital maps of sewerage networks. In this paper, we propose a generic data modelling for data fusion purposes taking into consideration the uncertainty aspects related to the collected data by allowing a confidence value for each data source and for each single data provided by a source.