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Analysing Multi-dimensional Data Across Autonomous Data Warehouses

Autoren: S. Berger, M. Schrefl
Paper: Berg06a (2006)
Zitat: A Min Tjoa, Juan C. Trujillo (eds.): Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2006), September 4-8, 2006, Krakow, Poland, Springer Verlag, Lecture Notes in Computer Science (LNCS) Vol. 4081, ISBN 3-540-37736-0, pp. 120-133, 2006.
Ressourcen: Kopie  (Senden Sie ein Email mit  Berg06a  als Betreff an dke.win@jku.at um diese Kopie zu erhalten)


Business cooperations frequently require to analyse data across enterprises, where there is no central authority to combine and manage cross-enterprise data. Thus, rather than integrating independent data warehouses into a Distributed Data Warehouse (DDWH) for crossenterprise analyses, this paper introduces a multi data warehouse OLAP language for integrating, combining, and analysing data from several, independent data warehouses (DWHs). The approach may be best compared to multi-database query languages for database integration.The key difference to these prior works is that they do not consider the multi-dimensional organisation of data warehouses.

The major problems addressed and solutions provided are: (1) a classification of DWH schema and instance heterogeneities at the fact and dimension level, (2) a methodology to combine independent data cubes taking into account the special characteristics of conceptual DWH schemata, i.e., OLAP dimension hierarchies and facts, and (3) a novel query language for bridging these heterogeneities in cross-DWH OLAP queries.

Schlagwörter: distributed Data Warehousing, distributed OLAP, multi-dimensional data integration