Introduction Information is one of the most valuable assets of an organization and when used properly can assist intelligent decision making that can significantly improve the functioning of an organization. Data warehousing is a recent technology that allows information to be easily and efficiently accessed for decision making activities. On-Line Analytical Processing (OLAP) tools are wellstudied for complex data analysis. A data warehouse is a set of subject-oriented, integrated, time varying, and non-volatile databases used to support the decision-making activities [42]. The conceptual architecture of a data warehousing system is shown in Figure 1. The data Information Sources - Relational -Legacy Warehouse Meta Data - Select -Transform -Clean -Integrate -Refresh -Others -Network Data OLAP Server MOLAP ROLAP/ Clients Front-end Tools -Analysis -Data Mining -Report generator - Others and Management Component Warehouse Creation

Efficient query processing is a critical requirement for data warehousing systems as decision support applications often require minimum response times to answer complex, ad-hoc queries having aggregations, multi-ways joins over vast repositories of data. This can be achieved by fragmenting warehouse data. The data fragmentation concept in the context of distributed databases aims to reduce query execution time and facilitates the parallel execution of queries. In this paper, we propose a methodology for applying the fragmentation technique in a Data Warehouse (DW) star schema to reduce the total query execution cost. We present an algorithm for fragmenting the tables of a star schema. During the fragmentation process, we observe that the choice of the dimension tables used in fragmenting the fact table plays an important role on overall performance. Therefore, we develop a greedy algorithm in selecting "best" dimension tables. We propose an analytical cost model for executing a set of OLAP queries on a fragmented star schema. Finally, we conduct some experiments to evaluate the utility of fragmentation for efficiently executing OLAP queries. Key Words : Data Warehouses, Star schema, Fragmentation, Query Optimization, Performance Evaluation 1

With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its k parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority.

XML data warehouses form an interesting basis for decisionsupport applications that exploit heterogeneous data from multiple sources. However, XML-native database systems currently suffer from limited performances in terms of manageable data volume and response time for complex analytical queries. Fragmenting and distributing XML data warehouses (e.g., on data grids) allow to address both these issues. In this paper, we work on XML warehouse fragmentation. In relational data warehouses, several studies recommend the use of derived horizontal fragmentation. Hence, we propose to adapt it to the XML context. We particularly focus on the initial horizontal fragmentation of dimensions’ XML documents and exploit two alternative algorithms. We experimentally validate our proposal and compare these alternatives with respect to a unified XML warehouse model we advocate for.