Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "7949"
#_source: array:26 [
"id" => "7949"
"slug" => "7949-combining-objects-with-rules-to-represent-aggregation-knowledge-in-data-warehouse-and-olap-systems 
7949-combining-objects-with-rules-to-represent-aggregation-knowledge-in-data-warehouse-and-olap-syst "
"yearMonth" => "2009-12"
"year" => "2009"
"title" => "Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems"
"description" => "PRAT, N., COMYN-WATTIAU, I. et AKOKA, J. (2009). <i>Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems</i>. DR-09014, ESSEC Business School. PRAT, N., COMYN-WATTIAU, I. et AKOKA, J. (2009). <i>Combining Objects with Rules to Represent Aggreg "
"authors" => array:3 [
0 => array:3 [
"name" => "PRAT Nicolas"
"bid" => "B00000434"
"slug" => "prat-nicolas"
]
1 => array:3 [
"name" => "COMYN-WATTIAU Isabelle"
"bid" => "B00000530"
"slug" => "wattiau-isabelle"
]
2 => array:2 [
"name" => "AKOKA Jacky"
"bid" => "B00714200"
]
]
"ouvrage" => ""
"keywords" => array:5 [
0 => "Agrégation"
1 => "Entrepôt de données"
2 => "Modèle conceptuel multidimensionnel"
3 => "Règle de production"
4 => "UML"
]
"updatedAt" => "2020-12-17 21:00:33"
"publicationUrl" => null
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "Documents de travail"
"en" => "Working Papers"
]
"support_type" => array:2 [
"fr" => "Editeur"
"en" => "Publisher"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Les entrepôts de données reposent sur la modélisation multidimensionnelle. A l'aide d'outils OLAP, les décideurs analysent les données à différents niveaux d'agrégation. Il est donc nécessaire de représenter les connaissances d'agrégation dans les modèles conceptuels multidimensionnels, puis de les traduire dans les modèles logiques et physiques. Cependant, les modèles conceptuels multidimensionnels actuels représentent imparfaitement les connaissances d'agrégation, qui (1) ont une structure et une dynamique complexes et (2) sont fortement contextuelles. Afin de prendre en compte les caractéristiques de ces connaissances, nous proposons de les représenter avec des objets (diagrammes de classes UML) et des règles en langage PRR (Production Rule Representation). Les connaissances d'agrégation statiques sont représentées dans les digrammes de classes, tandis que les règles représentent la dynamique (c'est-à-dire comment l'agrégation peut être effectuée en fonction du contexte). Nous présentons les diagrammes de classes, ainsi qu'une typologie et des exemples de règles associées. Les entrepôts de données reposent sur la modélisation multidimensionnelle. A l'aide d'outils OLAP, l "
"en" => "Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in Production Rule Representation (PRR) language. Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a data warehouse project. Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) t "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-03-30T07:21:41.000Z"
"docTitle" => "Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems"
"docSurtitle" => "Working Papers"
"authorNames" => "<a href="/cv/prat-nicolas">PRAT Nicolas</a>, <a href="/cv/wattiau-isabelle">COMYN-WATTIAU Isabelle</a>, AKOKA Jacky <a href="/cv/prat-nicolas">PRAT Nicolas</a>, <a href="/cv/wattiau-isabelle">COMYN-WATTIAU Isabelle</ "
"docDescription" => "<span class="document-property-authors">PRAT Nicolas, COMYN-WATTIAU Isabelle, AKOKA Jacky</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2009</span> <span class="document-property-authors">PRAT Nicolas, COMYN-WATTIAU Isabelle, AKOKA Jacky</span><br> "
"keywordList" => "<a href="#">Agrégation</a>, <a href="#">Entrepôt de données</a>, <a href="#">Modèle conceptuel multidimensionnel</a>, <a href="#">Règle de production</a>, <a href="#">UML</a> <a href="#">Agrégation</a>, <a href="#">Entrepôt de données</a>, <a href="#">Modèle conceptuel multi "
"docPreview" => "<b>Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems</b><br><span>2009-12 | Working Papers </span> <b>Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP System "
"docType" => "research"
"publicationLink" => "<a href="#" target="_blank">Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems</a> <a href="#" target="_blank">Combining Objects with Rules to Represent Aggregation Knowledge in Data "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.966824
+"parent": null
}
