Data.Warehouses
In the context of data warehouses it is very important to represent and consider
time. Representing time in data warehouses means to allow one to compare data
in different periods, that is, to consider the data evolution. The time dimension can
be related either to the validity of information (valid time), or to the presence of
information in the warehouse (transaction time). Moreover, the time dimension can
be related to the evolution of data with respect to the time. In this case it is possible
to store, in the data warehouse, successive versions of information.
In this section, we describe different approaches to consider and represent temporal
information in a data warehouse (Eder & Koncilia, 2001; Eder, Koncilia &
Morzy, 2001). Moreover, we focus on the representation of successive versions of
a document (B?™bel et al., 2004; Marian et al., 2001; Wang & Zaniolo, 2003; Wang,
Zaniolo, Zhou, & Moon, 2005).
In Eder and Koncilia (2001), the authors propose an approach for representing changes
in data dimensions of multidimensional data warehouses, by introducing temporal
extension, structure versioning, and transformation functions. The proposed model
is an extension of the multidimensional data model (Li & Wang, 1996; Vassiliadis
& Sellis, 1999), and allows one to represent the valid time dimension by means of
the time stamping of data.
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