Analyse multigraduelle OLAP

National audienceDecisional systems are based on multidimensional databases improving OLAP analyses. The paper describes a new OLAP operator named « BLEND » to perform multigradual analyses. The operation transforms multidimensional structures during querying in order to analyse measures according to various granularity levels, which are reorganised into a single parameter. We study valid combinations of the operation in the context of strict hierarchies. First experimentations implement the operation in an R-OLAP framework showing the slight cost of this operation

Automatic Detection of Structural Changes in Data Warehouses

Data Warehouses provide sophisticated tools for analyzing complex data online, in particular by aggregating data along dimensions spanned by master data. Changes to these master data are a frequent threat to the correctness of OLAP results, in particular for multi-period data analysis, trend calculations, etc. As dimension data might change in underlying data sources without notifying the data warehouse we are exploring the application of data mining techniques for detecting such changes and contribute to avoiding incorrect results of OLAP queries