Paper Info
Title
A Knowledge-driven Data Warehouse Model for Analysis Evolution
Abstract
A data warehouse is built by collecting data from external sources. Several changes on contents and structures can usually happen on these sources. Therefore, these changes have to be reflected in the data warehouse using schema updating or versioning. However a data warehouse has also to evolve according to new users' analysis needs. In this case, the evolution is rather driven by knowledge than by data. In this paper, we propose a Rule-based Data Warehouse (R-DW) model, in which rules enable the integration of users' knowledge in the data warehouse. The R-DW model is composed of two parts: one fixed part that contains a fact table related to its first level dimensions, and a second evolving part, defined by means of rules. These rules are used to dynamically create dimension hierarchies, allowing the analysis contexts evolution, according to an automatic and concurrent way. Our proposal provides flexibility to data warehouse's evolution by increasing users' interaction with the decision support system.
Year
Venue
Keywords
2006
ISPE CE
knowledge-driven data warehouse model,decision support system,data warehouse,fact table,analysis evolution,dimension hierarchy,external source,fixed part,r-dw model,rule-based data warehouse,analysis need,analysis contexts evolution,rule based,knowledge
Field
DocType
Volume
Data warehouse,Data transformation,Fact table,Computer science,Decision support system,Dimensional modeling,Schema evolution,Schema (psychology),Database,Software versioning
Conference
143
ISSN
ISBN
Citations 
0922-6389
1-58603-651-3
7
PageRank 
References 
Authors
0.52
12
3
Name
Order
Citations
PageRank
Cécile Favre111014.96
Fadila Bentayeb219836.79
Omar Boussaid331246.88