Abstract
Since fifteen years ago, data warehousing and On-Line analysis (OLAP) technologies proved their effectiveness in managing and analyzing huge amounts of structured and semi structured data. A data warehouse is usually queried in various combinations involving many tables. The most used operations are joins, aggregations and selections. Joins are well known to be expensive operations, especially when the involved relations are substantially larger than the size of the main memory which is usually the case of business intelligence applications. Without efficient optimization techniques, such queries may take hours or days, which is unacceptable in most cases. As a consequence, the physical design needs to address the challenges in efficient processing of complex decision support queries [1]. To optimize these queries, in addition to the existing ones (developed for OLTP applications), a large spectrum of optimization techniques were proposed in the literature and mostly supported by commercial database systems (ORACLE, SQL Server, DB2, etc.). We can cite materialized views, partitioning, advanced indexing schemes, parallel processing, etc. The aim of this special issue of the Distributed and Parallel Databases is twofold. First is to present new and challenging issues in the field of physical data warehouse design (in centralized, distributed and parallel environments). Secondly is to present the current research and technological development in this field. There was great response to the call for papers, but due to the limited space only five papers were accepted and selected for this special issue. These papers are authored by an outstanding roster of experts in their respective fields, and tackle various issues from different angles, requirements and interests. Their topics include data partitioning (relational and XML), processing queries on private data in untrusted environments, parallel processing, load balancing management. The evaluation process
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