Anne Tchounikine

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When changes occur on data organization, conventional multidimensional structures are not adapted because dimensions are supposed to be static. In many cases, especially when time covered by the data warehouse is large, dimensions of the hypercube must be redesigned in order to integrate evolutions. We propose an approach allowing to track history but also(More)
Data warehouses store large volumes of data according to a multidimensional model with dimensions representing different axes of analysis. OLAP systems (online analytical processing) provide the ability to interactively explore the data warehouse. Rising volumes and complexity of data favor the use of more powerful distributed computing architectures.(More)
In field such as Cardiology, data used for clinical studies is not only alphanumeric, but can also be composed of images or signals. Multimedia data warehouse then must be studied in order to provide an efficient environment for the analysis of this data. The analysis environment must include appropriate processing methods in order to compute or extract the(More)
Data warehouses store large volumes of data according to a multidimensional model that provides a fast access for online analysis. The constant growth in quantity and complexity of data stored in data warehouses has led to a variety of data warehouse applications on distributed systems. The main benefits of these architectures are parallelized query(More)
Data warehouses and OLAP systems help to interactively analyze huge volumes of data. Spatial OLAP refers to the integration of spatial data in multidimensional applications at the physical, logical and conceptual level. In order to include spatial information as a result of the decision-making process, we propose to define spatial measures as geographical(More)