适应性数据分割

Adaptive Data Partitioning

应用适应性数据分割技术将源数据分为几部分,每一部分通过不同的、互补的计划来完成。将这个新技术应用到模型中,不仅能修正基数值和被低估的选择性,而且也能发现和使用源数据的序,且能更有效的使用预集合的源数据。

This paper presents a generalized architecture for adaptive query processing and introduce a new technique, called adaptive data partitioning （ADP） , which is based on the idea of dividing the source data into regions, each executed by different, complementary plans. It shows this model can be applied in novel ways to not only correct for underestimated selectivity and cardinality values,but also to discoveres and exploits order in the source data,and to detect and exploit source data that can be effectively pre- aggregated.