欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
时间序列数据的高维性是影响数据查询代价的主要因素,降维技术是时间序列数据查询优化的有效手段。原有降维技术近似体积的无界性,造成索引阶段不能充分实现点过滤,影响了查询效率。首先,利用近似体积有界的非线性降维技术解决了点过滤问题。然后,引入提前终止技术,减少了原始序列距离计算阶段的冗余计算。在此基础上,提出排序的子序列相似查询算法。实验结果表明,排序子序列相似查询方法具有较高的效率。
英文摘要：
High dimensionality of time series data cause high query cost. Dimensionality reduction on the data is an effective way ofqueryoptimization. The approximation volume ofproposed dimensionality reduction is unbounded, so the algorithm can not effectively filter the point in the index. First, a new non-linear dimensionality reduction is used to resolve the problem. Second, Early abandon technique is introduced to further improve efficiency and reduce redundant computation. Then on these basis, ranked subsequence similarity search algorithm is proposed. The experimental results show that ranked algorithm has higher efficiency.
同期刊论文项目
同项目期刊论文
期刊信息
位置：