Data warehouses are a primary means for a consolidated view on the data within an enterprise and frequently a first step in integrating enterprise information systems. Above all, data warehouses are used for analyzing enterprise data online, giving the possibility to aggregate and compare data along dimensions relevant in the application domain. Typically time is one of the dimensions we find in data warehouses allowing comparisons of different periods. The instances of dimensions, however, change over time - countries unite and separate, products emerge and vanish, organizational structures evolve. In current data warehouse technology these changes cannot be represented adequately since all dimensions are (implicitly) considered as orthogonal, putting heavy restrictions on the validity of OLAP queries spanning several periods. In this paper we briefly propose an architecture for temporal data warehouse systems which allows the registration of temporal versions of dimension data and the transfer of data between different temporal versions.

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