Complex Data Warehousing and Knowledge Discovery for by Tho Manh Nguyen

By Tho Manh Nguyen

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Different goals can be achieved by using a benchmark: (1) compare the performance of various systems in a given set of experimental conditions (users); (2) evaluate the impact of architectural choices or optimization techniques on the performance of one given system (system designers). In a 30 data warehouse benchmark, the workload may be subdivided into: (1) decision-support queries, mostly OLAP queries such as cube, roll-up, drill down and slice and dice; (2) queries used during the ETL (Extract Transform Load) process.

This is especially critical when performing tasks, such as computing data cubes or performing data mining. Generally, to evaluate the efficiency of performance optimization techniques, (such as index and materialized view techniques), one should design a data warehouse benchmark. This latter may be defined as a database model and a workload model (set of queries to execute on the database). Different goals can be achieved by using a benchmark: (1) compare the performance of various systems in a given set of experimental conditions (users); (2) evaluate the impact of architectural choices or optimization techniques on the performance of one given system (system designers).

In Tera-scale data warehousing environments, it would certainly not be. Figure 6 describes an alternative search strategy that is tailored to the unique structure of the packed R-tree. We refer to this strategy as Linear Breadth First Search to emphasize the mapping to the physical linearization on disk. In general, the algorithm follows the standard BFS technique of traversing the tree level-by-level in a left-to-right fashion. Queries are resolved as follows. For the current level i of the tree, the query engine successively identifies the j nodes at level i - 1 whose bounding boxes intersect the query Φ = {r1, r2, …, rd}.

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