Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "4457"
#_source: array:26 [
"id" => "4457"
"slug" => "4457-combining-view-integration-and-schema-clustering-to-improve-database-design"
"yearMonth" => "1998-10"
"year" => "1998"
"title" => "Combining View Integration and Schema Clustering to Improve Database Design"
"description" => "COMYN-WATTIAU, I., AKOKA, J. et KEDAD, Z. (1998). Combining View Integration and Schema Clustering to Improve Database Design. Dans: <i>BDA'98. Quatorzièmes Journées Bases de Données Avancées</i>. Laboratoire PriSM, Université de Versailles, pp. 59-80. 
COMYN-WATTIAU, I., AKOKA, J. et KEDAD, Z. (1998). Combining View Integration and Schema Clustering t "
"authors" => array:3 [
0 => array:3 [
"name" => "COMYN-WATTIAU Isabelle"
"bid" => "B00000530"
"slug" => "wattiau-isabelle"
]
1 => array:2 [
"name" => "AKOKA Jacky"
"bid" => "B00714200"
]
2 => array:1 [
"name" => "KEDAD Z."
]
]
"ouvrage" => "BDA'98. Quatorzièmes Journées Bases de Données Avancées"
"keywords" => []
"updatedAt" => "2021-04-19 17:57:25"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => "59-80"
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Actes d'une conférence"
"en" => "Conference Proceedings"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Cet article décrit une approche combinée de deux techniques utilisées en conception de bases de données : l'intégration de vues et la classification de schémas. Les méthodes d'intégration de vues usuelles traitent principalement les problèmes de terminologie, de recouvrement de classes, de contradictions de contraintes et d'équivalences de représentations. Cependant deux autres types de problèmes restent sans solution. Le premier est la comparaison deux à deux des concepts des deux vues qui peut conduire à un processus très coûteux mettant en place la confrontation exhaustive de tous ces concepts : n2 comparaisons pour des vues comprenant chacune n concepts. Nous proposons une technique de classification automatique, qui permet de réduire le nombre de comparaisons élémentaires à n2/4 dans le pire des cas et à n dans le cas le plus favorable. Le second problème est la validation de l'intégration. En effet, le processus d'intégration conduit à un schéma global amalgamant toutes les vues initiales. La validation consiste à vérifier que ce schéma contient effectivement chacune des vues initiales. Là encore, nous proposons d'appliquer la même technique de classification avec des distances sémantiques spécifiques. Les deux utilisations de la classification automatique ont fait l'objet du développement d'un prototype qui a permis de tester la technique sur plusieurs exemples de vues de grande taille. Cet article décrit une approche combinée de deux techniques utilisées en conception de bases de donn "
"en" => "The aim of this paper is to provide a coherent combination of two well-known techniques improving database design : view integration and schema clustering. View integration methods suggested in the literature tend to address mainly the following problems: terminology problems, class definition overlappings, constraint contradictions, different representations of concepts. However two other problems remain unsolved. The first one is concerned with view comparison, which may lead to a very costly process requiring, for two views containing about n objects, up to n2 elementary comparisons. This paper proposes a clustering technique for solving this problem. The clustering is based on an automatic process, reducing the number of elementary comparisons to n2/4 in the worst case and n in the best case. The second problem is related to view integration validation. The view integration process leads to a global schema amalgamating all the initial views. View integration validation consists mainly in checking that this objective is met. This paper proposes an application of automatic clustering to view integration permitting to recover the initial views. In order to validate view integration, the clustering algorithm requires the application of several semantic distances taking into account different integration situations. We propose to perform the clustering process using successively several distances until a satisfying partitioning is obtained. The two techniques have been applied to several case studies leading to empirical but very promising results. The aim of this paper is to provide a coherent combination of two well-known techniques improving da "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-03-30T06:21:40.000Z"
"docTitle" => "Combining View Integration and Schema Clustering to Improve Database Design"
"docSurtitle" => "Conference Proceedings"
"authorNames" => "<a href="/cv/wattiau-isabelle">COMYN-WATTIAU Isabelle</a>, AKOKA Jacky, KEDAD Z."
"docDescription" => "<span class="document-property-authors">COMYN-WATTIAU Isabelle, AKOKA Jacky, KEDAD Z.</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">1998</span> <span class="document-property-authors">COMYN-WATTIAU Isabelle, AKOKA Jacky, KEDAD Z.</span><br><spa "
"keywordList" => ""
"docPreview" => "<b>Combining View Integration and Schema Clustering to Improve Database Design</b><br><span>1998-10 | Conference Proceedings </span> <b>Combining View Integration and Schema Clustering to Improve Database Design</b><br><span>1998-10 "
"docType" => "research"
"publicationLink" => "<a href="#" target="_blank">Combining View Integration and Schema Clustering to Improve Database Design</a> <a href="#" target="_blank">Combining View Integration and Schema Clustering to Improve Database Des "
]
+lang: "en"
+"_type": "_doc"
+"_score": 9.019018
+"parent": null
}
