Summary form only given. In order to prepare complex data for relevant analysis, a data warehousing-based approach is needed. However, a good multidimensional modeling requires efficient preparation of data starting with a data integration phase. We present in this paper two principal steps of the complex data warehousing process. The data integration is the first one. To do that, we define a generic UML data model capable of representing a wide range of complex data including their possible semantic properties. Furthermore, complex data are represented as XML documents generated through an implemented prototype. The second important phase is the preparation of data for the multidimensional modeling. We demonstrate that we can use data mining techniques to help the user in building a better multidimensional model.