In recent years the database community has experienced a tremendous increase in the availability of new technologies to support efficient storage and retrieval of large volumes of data, namely data warehousing and On-Line Analytical Processing (OLAP) products. Efficient query processing is critical in such an environment, yet achieving quick response times with OLAP queries is still largely an open issue. In this paper we propose a solution approach to this problem by applying parallel processing techniques to a warehouse environment. We suggest an efficient partitioning strategy based on the relational representation of a data warehouse (i.e., star schema). Furthermore, we incorporate a particular indexing strategy, DataIndexes, to further improve query processing times and parallel resource utilization, and propose a preliminary parallel star-join strategy.

Two possible strategies may be utilized to enhance the efficiency of processing OLAP queries: (a) precomputation strategies (e.g., view materialization, realizing data cubes), and (b) ad-hoc strategies. While a significant amount of work has been done in developing precomputation strategies, it is generally recognized that it is difficult to materialize the answers to all possible queries. Thus, ad-hoc querying must be supported in data warehouses. This realization has sparked an interest in exploring indexing strategies suitable for OLAP queries. There appears to have been relatively little work done in ad-hoc query support for data warehouses [45, 46, 55, 39]. In this paper we propose DataIndexes as a new paradigm for storing the base data. An attractive feature of DataIndexes is that they serve as indexes as well as the store of base data. Thus, DataIndexes actually define a physical design strategy for a data warehouse where the indexing, for all intents and purposes, comes for "fr...

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