A Model-Driven Heuristic Approach for Detecting Multidimensional Facts in Relational Data Sources

Lucentia Research Group Dept. of Software and Computing Systems, University of Alicante, Spain
DOI: 10.1007/978-3-642-15105-7_2
Source: DBLP

ABSTRACT Facts are multidimensional concepts of primary interests for knowledge workers because they are related to events occurring
dynamically in an organization. Normally, these concepts are modeled in operational data sources as tables. Thus, one of the
main steps in conceptual design of a data warehouse is to detect the tables that model facts. However, this task may require
a high level of expertise in the application domain, and is often tedious and time-consuming for designers. To overcome these
problems, a comprehensive model-driven approach is presented in this paper to support designers in: (1) obtaining a CWM model
of business-related relational tables, (2) determining which elements of this model can be considered as facts, and (3) deriving
their counterparts in a multidimensional schema. Several heuristics –based on structural information derived from data sources–
have been defined to this end and included in a set of Query/View/Transformation model transformations.

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Available from: Stefano Rizzi, Aug 14, 2015
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    • "Classification Hiérarchique [3] [10] Notre proposition Autre [4, 12] [8] TAB. 1 – Récapulatif de l'état de l'art sur la construction de hiérarchies des heuristiques [2]. Concernant les dimensions, d'autres travaux proposent d'utiliser les métadonnées logiques d'une base de données comme par exemple les clés étrangères [5]. "
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