Abstract
Data warehouses are now widely used for analysis and decision support purposes. The availability of software solutions, which are more and more user-friendly and easy to manipulate has made it possible to extend their use to end users who are not specialists in the field of business intelligence. The purpose of this article is to provide an approach that assists non-expert users in the data warehouse design process and integrates their contextual data. As well as to provide a method that assists non-expert users in data warehouse design process while incorporating their contextual data. Our proposal consists of a context model and a comprehensive Data Warehouse construction method that attaches the context to data warehouses and uses it to produce customized data marts adapted to the decision makers context.
Highlights
Recent developments in Business Intelligence (BI) area have been marked by the availability of numerous software solutions combining functional richness, user-friendliness and easiness to use by end-users
The model we have proposed combines two definitions, the first is the "definition by enumeration" proposed in [27] and [28], the second is the one proposed by [26] who defines a model of context as a list of couples of elements {(Attribute, Value)}
We presented a data warehouse designing solution that is intend to support non-expert users while taking into account and integrating their contexts into the data warehouse
Summary
Recent developments in Business Intelligence (BI) area have been marked by the availability of numerous software solutions combining functional richness, user-friendliness and easiness to use by end-users. Today BI software solutions provide, in addition to their basic functions such as data extraction, transformation and load, a rich and interactive catalog of data processing and visualization features. This has led businesses to enlarge the use of BI solutions at various levels of responsibility and to cover plenty of functions and work positions. The purpose of this work is to address these two issues, we provide a method that assist non-expert users while integrating their contextual data into Data Warehouse design process to produce contextualized data marts. We conclude and propose some tracks for our future work
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