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Analysing Multi-dimensional Data Across Autonomous Data Warehouses

Authors: S. Berger, M. Schrefl
Paper: Berg06a (2006)
Citation: 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.
Resources: Copy  (In order to obtain the copy please send an email with subject  Berg06a  to


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