Methodology for Improving Data Warehouse Design using Data Sources Temporal Metadata

Abstract

One of the most complex issues of the integration and transformation interface is the case where there are multiple sources for a single data element in the enterprise Data Warehouse (DW). There are many facets due to the number of variables that are needed in the integration phase. This chapter presents our DW architecture for temporal integration on the basis of the temporal properties of the data and temporal characteristics of the data sources. If we use the data arrival properties of such underlying information sources, the Data Warehouse Administrator (DWA) can derive more appropriate rules and check the consistency of user requirements more accurately. The problem now facing the user is not the fact that the information being sought is unavailable, but rather that it is difficult to extract exactly what is needed from what is available. It would therefore be extremely useful to have an approach which determines whether it would be possible to integrate data from two data sources (with their respective data extraction methods associated). In order to make this decision, we use the temporal properties of the data, the temporal characteristics of the data sources, and their extraction methods. In this chapter, a solution to this problem is proposed.

Modelling Spatial Decision Support Systems Focused on the Development of a Data Warehouse

“…In (Malinowski, et al, 2006b) they consider four phases in the development of DW: specification of requirements, conceptual, logical and physical. In (Araque et al, 2009) they define a methodology to improve the design of the DW focused on the conceptual phase, and particularly during the ETL process. This happens in addition to ways of integrating data from different sources, and where each source, may have its own extraction methods.…”