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Méthodologie d'évaluation de la cohérence inter-représentations pour l'intégration de bases de données spatiales. Une approche combinant l'utilisation de métadonnées et l'apprentissage automatique.

Abstract : Nowadays most databases are run independently. An independence that leads to a series of
problems: repeated efforts of maintenance and updating, difficulty in proceeding with an analysis at
various levels and no guarantee of coherence between sources.
Joint management of these sources requires them to be integrated in order to define the explicit
links between the various bases and to provide a unified vision. Our thesis deals with this issue. It
concentrates in particular on the means of relating data and of assessing coherence between multiple
representations. We have sought to systematically analyse each difference in representation between
matching data so as to determine whether it results from different criteria used for data capture or from
errors in the capture itself, the aim being to ensure coherent data integration.
In order to study the conformity of representations, we suggest exploiting existing database
specifications. These documents describe specific selection and modelling rules for objects. They are
reference metadata used to determine whether representations are equivalent or incoherent. But their
use is insufficient since specifications described in a natural language can be imprecise or incomplete.
So the data contained in the bases is a second interesting source of knowledge. If one uses machine
learning techniques to analyse how they tally, it becomes possible to establish evaluation rules that
enable a justification of the conformity of representations.
The methodology we put forward is based upon these elements. It consists in a coherence
evaluation process and a knowledge acquisition proceeding. The process comprises several steps: data
enrichment, intra-base control, matching, inter-bases control, and the final assessment. Each of these
steps exploits knowledge inferred from the specifications or induced from the data through learning.
The benefit of using machine learning techniques is twofold: not only does it enable to acquire
evaluation rules, it also reveals the discrepancy tolerated in the data when compared to the written
specifications.
This approach has been carried out on NGI databases that showed different levels of detail.
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Contributor : David Sheeren <>
Submitted on : Thursday, July 13, 2006 - 1:29:38 PM
Last modification on : Tuesday, May 4, 2021 - 6:13:19 PM
Long-term archiving on: : Monday, April 5, 2010 - 10:18:33 PM

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  • HAL Id : tel-00085693, version 1

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David Sheeren. Méthodologie d'évaluation de la cohérence inter-représentations pour l'intégration de bases de données spatiales. Une approche combinant l'utilisation de métadonnées et l'apprentissage automatique.. Autre [cs.OH]. Université Pierre et Marie Curie - Paris VI, 2005. Français. ⟨tel-00085693⟩

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