Description: The research paper Data Warehouse Striping Technique (DWS) talks about Handling Big Dimensions in Data Warehouses using the striping technique. The research paper suggests that the DWS technique enables distribution of large data houses through a cluster of computers. The data partitioning technique partitions information and sends them across different nodes or access points. Dimension tables are also replicated. The replication of the dimension tables poses a restraint to the applicability of the DWS technique to data warehouses with big dimensions. This paper proposes a strategy to handle large dimensions in a distributed DWS system and evaluates the proposed strategy experimentally.
The DWS technique relies upon the typical star schema and the typical data warehouse queries to optimize the way data is partitioned among the computers in the DWS system and the way queries are distributed and executed. The typical infrastructure required to implement DWS is a cluster of inexpensive computers connected via Ethernet and a DBMS installed in each node. All the nodes have a same star schema and dimensions are replicated in each node. The fact data is also installed to share the facts at each node. The uniform partitioning is necessary to assure optimal load balance and to facilitate the computation of confidence intervals in the cases (rare, in principle) when one or more nodes in the system are not available.
Conclusion: The research paper concludes over a note saying that a new technique called the ‘selective’ load is developed to overcome the limitations of the DWS technique in handling Data Warehouses. The suggested technique helps managing data warehouses more effectively and efficiently, maintaining nearly linear speed up in query execution time. The experiments performed with the TPC-H schema and queries have suggested that the selective load improves dramatically the performance of a DWS system when processing queries in data warehouse scheme with potentially big dimensions.
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