Skip to Main content Skip to Navigation

Harmonisation de l'information géo-scientifique de bases de données industrielles par mesures automatiques de ressemblance

Abstract : In order to harmonize industrial seismic navigation data bases, a methodology and a software have been developed. The methodology of Similarity Measurement Automation provides protocols to build a model and a hierarchy for the comparison criteria that shall be used as points of reference for the automation. With its tolerance set of thresholds, the model has been used as a scaled filter within the automatic classification process which aim is to find as quickly as possible very similar data. Similarity is measured by combinations of elementary metrics giving scores, and also by a global and contextual procedure, giving access to three levels of results: similarity between attributes, between individuals, and between groups. Accurate automated analyses of the expert system as well as human interpretations on multiple criteria are now possible thanks to these similarity estimations, reducing to two days instead of three weeks the work of a geophysicist. Classification strategies have been designed to suit the different data management issues, as well as harmonization, reconciliation or geo-referencing. The methodology has been implemented in software for automatic comparisons named LAC, and developed for Data Management and Technical Documentation services in TOTAL. The software has been industrialized and has been used for three years, even if now there is no technical maintenance anymore. The last data base visualization functionalities that have been developed have not been integrated yet to the software, but shall provide a better visualization of the phenomena. This latest way to visualize data is based on similarity measurement and obtains an image of complex and voluminous data clear enough. It also puts into relief information useful for harmonization and data quality evaluation. Would it be possible to characterize, compare, analyze and manage data flows, to monitor their evolution and figure out new machine learning methods by developing further this kind of data base imaging?
Document type :
Complete list of metadatas
Contributor : Abes Star :  Contact
Submitted on : Monday, January 7, 2019 - 1:01:08 AM
Last modification on : Wednesday, October 14, 2020 - 4:10:53 AM
Long-term archiving on: : Monday, April 8, 2019 - 1:47:40 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01971352, version 1


Alba Fuga. Harmonisation de l'information géo-scientifique de bases de données industrielles par mesures automatiques de ressemblance. Sciences de la Terre. Université Pierre et Marie Curie - Paris VI, 2017. Français. ⟨NNT : 2017PA066184⟩. ⟨tel-01971352⟩



Record views


Files downloads