Mapping and integration of dimensional attributes using clustering techniques

Abstract

Following recent trends in Data Warehousing, companies realized that there is a great potential in combining their information repositories to obtain a broader view of the economical market. Unfortunately, even though Data Warehouse (DW) integration has been defined from a theoretical point of view, until now no complete, widely used methodology has been proposed to support the integration of the information coming from heterogeneous DWs. This paper deals with the automatic integration of dimensional attributes from heterogeneous DWs. A method relying on topological properties that similar dimensions maintain is proposed for discovering mappings of dimensions, and a technique based on clustering algorithms is introduced for integrating the data associated to the dimensions. © 2012 Springer-Verlag.