Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing st
udies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view-index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.
In decision-support systems, the visual component is important for On Line Analysis Processing (OLAP). In this paper, we propose a new approach that faces the visualization problem due to data sparsity. We use the results of a Multiple Correspondence
Analysis (MCA) to reduce the negative effect of sparsity by organizing differently data cube cells. Our approach does not reduce sparsity, however it tries to build relevant representation spaces where facts are efficiently gathered. In order to evaluate our approach, we propose an homogeneity criterion based on geometric neighborhood of cells. The obtained experimental results have shown the efficiency of our method.
