Blind Optimization for data Warehouse during design

Design a suitable data warehouse is getting increasingly complex and requires more advance technique for different step. In this paper, we present a novel data driven approach for fragmentation based on the principal components analysis (PCA). Both techniques has been treated in many works [2][7]. The possibility of its use for horizontal and vertical fragmentation of data warehouses (DW), in order to reduce the time of query execution. We focus the correlation matrices, the impact of the eigenvalues evolution on the determination of suitable situations to achieve the PCA, and a study of criteria for extracting principal components. Then, we proceed to the projection of individuals on the first principal plane, and the 3D vector space generated by the first three principal components. We try to determine graphically homogeneous groups of individuals and therefore, a horizontal fragmentation schema for the studied data table. [PUBLICATION ABSTRACT]