Already Ovid claimed that everything is in flux and we see continuing metamorphoses. Knowledge about changes is essential for Knowledge management in particular for the correct interpretation of data stemming from different periods. Data Warehouses are increasingly deployed in public administrations to provide analytical data for decision making, for monitoring or for revisions. Changes in transaction data are recorded in data warehouses and sophisticated tools allow to analyze these data along time and other dimensions. But changes in master data and in structures cannot be represented in current data warehouse systems impeding their use in dynamic areas and leading to erroneous query results. For an example: if the definition of "unemployment rate" changes, then the figures cannot be compared to those of previous years. Trend calculations on basis of the available data is irrelevant or severely misleading. We propose a temporal data warehouse architecture named COMET to represent structural changes and permit correct analysis of data over periods with changing structures.

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