engleski

Automating the Process of Schema Integration for Heterogeneous Data Warehouses

This doctoral thesis proposes an approach for automated schema integration of heterogeneous data warehouses. A federated data warehouse is a logical integration of data warehouses applicable when physical integration is impossible due to privacy policy or legal restrictions. In order to enable the translation of queries in a federated approach, heterogeneous schemas of the federated and the local warehouses must be matched. The proposed schema integration procedure is capable of solving heterogeneities among data warehouse structures specific to the multidimensional conceptual model: facts, measures, dimensions, aggregation levels and dimensional attributes. Similarities between warehouse schema structures are computed by using semantic and structural comparison. Filter algorithms, based on bipartite graph matching, use the calculated similarity values for creating necessary mappings between multidimensional structures. Restriction rules are proposed for aggregation level matching, as the partial order in dimension hierarchies must be preserved. A software implementation of the entire process is provided in order to perform its verification and to determine the proper filter algorithms for mapping different multidimensional structures.

data warehouse; data warehouse integration; federated data warehouse; schema matching; multidimensional data model; semantic similarity; structure similarity; mapping; bipartite graphs

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