Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems
Jacky Akoka (),
Isabelle Comyn-wattiau () and
Nicolas Prat ()
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Jacky Akoka: CEDRIC-CNAM & TMSP, Postal: 292 rue St Martin, 75141 PARIS Cedex 03, FRANCE, http://cedric.cnam.fr
Isabelle Comyn-wattiau: CEDRIC-CNAM & ESSEC Business School, Postal: 292 rue St Martin, 75141 PARIS Cedex 03, FRANCE, http://cedric.cnam.fr
Nicolas Prat: ESSEC Business School, Postal: Avenue Bernard Hirsch - BP 50105, 95021 CERGY-PONTOISE Cedex, FRANCE, http://www.essec.edu
No DR 09014, ESSEC Working Papers from ESSEC Research Center, ESSEC Business School
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.
Keywords: Aggregation; Conceptual Multidimensional Model; Data Warehouse; On-line Analytical Processing (OLAP); Production Rule; UML (search for similar items in EconPapers)
JEL-codes: C88 M15 (search for similar items in EconPapers)
Pages: 10 pages
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