Abstract
Data warehouses (DW) must integrate information from the different areas and sources of an organization in order to extract knowledge relevant to decision-making. The DW development is not an easy task, which is why various design approaches have been put forward. These approaches can be classified in three different paradigms according to the origin of the information requirements: supply-driven, demand-driven, and hybrids of these. This article compares the methodologies for the multidimensional design of DW through a systematic mapping as research methodology. The study is presented for each paradigm, the main characteristics of the methodologies, their notations and problem areas exhibited in each one of them. The results indicate that there is no follow-up to the complete process of implementing a DW in either an academic or industrial environment; however, there is also no evidence that the attempt is made to address the design and development of a DW by applying and comparing different methodologies existing in the field.
Highlights
Data warehouses (DW) are a collection of an organization’s historical data of any kind
The results indicate that there is no follow-up to the complete process of implementing a DW in either an academic or industrial environment; there is no evidence that the attempt is made to address the design and development of a DW by applying and comparing different methodologies existing in the field
Cravero and Sepúlveda [32], perform a chronological study of various methodologies, but there is not a comparative study. They used a systematic methodology for the selection of papers. All these works present a study on methodologies for DW design, but do not use a methodology recognized by the software engineering community, such as the systematic mapping
Summary
Data warehouses (DW) are a collection of an organization’s historical data of any kind. The historical data are analyzed by the decision-makers by converting the data into strategic information in order to support the decisionmaking process [1] These DWs integrate a huge amount of data coming from heterogeneous data sources into a multidimensional design (MD). The study presents the main characteristics of the activities developed in the methodologies, as well as the notations and problem areas that each paradigm contains. It is with this motivation that this study arose from our work to compile, map and summarize the primary studies on methodologies for the MD in DW.
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