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Exploiting data access for dynamic fragmentation in data warehouse

The large size of a data warehouse and the complexity of OLAP queries constitute query performance challenges. Several techniques have been developed to reduce query response time. Data fragmentation improves significantly data management, accessibility and query execution time. Optimal fragmentation schema is designed from workload gathered from data exploitation. So, in context of relational-and object-oriented databases these techniques remain adapted because the workload is almost stable. However, the specific characteristics of data warehouse and more particularly the nature of OLAP queries makes data model and workload very dynamic and consequently an ineffective designed fragmentation schema. To achieve this problem, we propose in this paper an approach based on exploitation of recent statistical data access for dynamic data fragmentation in data warehouse.