Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
A Model-Driven Heuristic Approach for Detecting Multidimensional Facts in Relational Data Sources
verfasst von : Andrea Carmè, Jose-Norberto Mazón, Stefano Rizzi
Erschienen in: Data Warehousing and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.