A data warehouse (DW) collects large amounts of data from various data sources and transforms them to a form that can be used to analyze the behavior of an organization. DWs are based on the multidimensional model, which represents data as facts that can be analyzed along a collection of dimensions, composed of levels conforming aggregation hierarchies. The basic multidimensional model assumes that only facts evolve in time and this is materialized by the link(s) of the facts with the time dimension. However, dimension data may also vary across time, for instance, a product may change its price or its category. Furthermore, a measure itself can change its value, for instance, a client may request a change in the quantity of products of an order she previously placed. Temporal databases provide mechanisms for managing information that varies over time. The use of these mechanisms to provide built-in temporal semantics to DWs leads to the concept of temporal data warehouses.
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