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Cloud Computing provides a flexible environment for customers to host and process their information through an outsourced infrastructure. This information was habitually located on local servers. Many applications dealing with massive data is routed to the cloud. Data Warehouse (DW) also benefits from this new paradigm to provide analytical data online and in real time. DW in the Cloud benefited of its advantages such flexibility, availability, adaptability, scalability, virtualization, etc. Improving the DW performance in the cloud requires the optimization of data processing time. The classical optimization techniques (indexing, materialized views and partitioning) are still essential for DW in the cloud. However, the DW is partitioned before being distributed across multiple servers (nodes) in the Cloud. When query containing multiple joins or ask voluminous data stored on multiple nodes, inter-node communication increases and consequently the DW performance degrades. In this paper, we propose an approach for improving the performance of DW in the cloud. Our approach is based on selection of temporary materialized views through a web service. For this purpose we use an algorithm allows to identify the queries list sent to the DW, and adds a materialized view for each new costly frequent query. This technique is based on managing a temporary materialized views in order to optimize the frequent queries load by respecting the total cost. An experimental study on a cloud DW is carried out and a comparative tests show the satisfaction of our approach. |
