基于立方体计算的关键梯度分析

梯度分析是数据仓库和联机分析处理中的一项重要分析任务,在决策支持中发挥着重要作用.本文根据实际应用的需要,提出了一种新颖的关键梯度分析方法.借助立方体计算中的计数排序和分割策略,通过扩展补充路径,并利用插入排序方法,实现了高效的关键梯度分析算法.在模拟数据上进行了大量的实验,结果证明了算法的高效性和实用性.

Significant Gradients Mining Based on Data Cube Computation

Gradient analysis is an important data analysis task in data warehousing and online analytical processing, which has played an important role in the application of decision support. This paper considers a novel type of gradient analysis, significant gradient analysis. Significant gradient analysis is expressve, capable of capturing trends in data and answering "what-if" questions. The problem of mining significant gradients is challenging since the significant gradients can be widely scattered in the cube lattice, and do not present any monotonicity. To tackle the problem and develop techniques to speed up the search the state-of-the-art cube computation algorithm is extemded. An extensive perform- ance study is reported to illustrate the effect of the approach.