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Bitmap Techniques for Optimizing Decision Support Queries and Association Rule Algorithms

机译:用于优化决策支持查询和关联规则算法的位图技术

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In this paper, we discuss some new bitmap techniques for optimizing decision support queries and association rule algorithm. We first show how to use a new type of predefined bitmap join index (pre-join-bitmap-index) to efficiently execute complex decision support queries with multiple outer join operations involved and push the outer join operations from the data flow level to the bitmap level and achieve significant performance gain. Then we discuss a bitmap based association rule algorithm. Our bitmap based association rule algorithm Bit-AssocRule doesn 't follow the generation-and-test strategy of Apr lor i algorithm and adopts the divide-and-conquer strategy, thus avoids the time-consuming table scan to find and prune the item-sets, all the operations of finding large itemsetsfrom the datasets are the fast bit operations. The experimental results show Bit-AssocRule is 2 to 3 orders of magnitude faster than Apriori and AprioirHybrid algorithms. Our results indicate that bitmap techniques can greatly improve the performance of decision support queries and association rule algorithm, and bitmap techniques are very promising for the decision support query optimization and data mining applications.
机译:在本文中,我们讨论了一些新的位图技术,用于优化决策支持查询和关联规则算法。我们首先展示如何使用新类型的预定义位图连接索引(预连接位图 - 索引),以有效地执行具有多个外部连接操作的复杂决策支持查询,并将外部连接操作从数据流级推送到位图水平并实现重大的性能收益。然后我们讨论基于位图的关联规则算法。我们基于位图的关联规则算法比特协作人员遵循APR LOR I算法的生成和测试策略,采用分行和征服策略,从而避免耗时的表扫描查找和修剪项目 - 设置,从数据集中查找大型itemsets的所有操作都是快速位操作。实验结果显示比特 - 协会比APRiORI和Aproirohybrid算法快2到3个数量级。我们的结果表明,位图技术可以大大提高决策支持查询和关联规则算法的性能,而Bitmap技术对于决策支持查询优化和数据挖掘应用是非常有希望的。

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