【摘要】 子序列的相似性查询是时间序列数据集中的一种重要操作,包括范围查询和k近邻查询.现有的大多算法是基于欧几里德距离或者DTW距离的,缺点在于查询效率低下.文中提出了一种新的基于LSH的距离度量方法,可以在保证查询结果质量的前提下,极大提高相似性查询的效率;在此基础上,给出一种DS-Index索引结构,利用距离下界进行剪枝,进而还提出了两种优化的OLSH-Range和OLSH-kNN算法.实验是在真实的股票序列集上进行的,数据结果表明算法能快速精确地找出相似性查询结果.更多还原

【Abstract】 Subsequence Similarity Query is an important operation in time series,including range query and k nearest neighbor query.Most of these algorithms are based on Euclidean distance or DTW distance,weak point of which is the time inefficiencies.We propose a new distance measure,based on Locality Sensitive Hash(LSH),which improve the efficiency greatly while ensuring the quality of the query results.We also propose an index structure named DS-Index.Using DS-Index,we prune the candidates of query and thus propose two optimal algorithms: OLSH-Range and OLSH-kNN.Our experiments conducted on real stock exchange transaction sequence datasets show that algorithms can quickly and accurately find similarity query results.更多还原