标题:Efficient Similarity Searching Approach for Streaming Time Series
作者:Q., Zhang; W., Luo; Z., Hu; P., Zhan; Y., Jin; X., Li
作者机构:[3;3;3;3;3;3] School of Software, Shandong University, Jinan, China;[] Research Center of Geotechnical Structural Engineering, Shandong University, Ji 更多
会议名称:9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018
会议日期:23 November 2018 through 25 November 2018
来源:Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
出版年:2019
卷:2018-November
页码:815-818
DOI:10.1109/ICSESS.2018.8663834
关键词:Data Mining; Indexing and Retrieval; Similarity Measure; Streaming Time series
摘要:Similarity searching is a method for measuring the correlation of the pair of subsequences in streaming time series, which also aims to find all subsequences which are similar to the given one. However, in the burgeoning of IoE (Internet of Everything), massive numbers of loT devices in entensive fields are continuously producing huge number of time series, named as streaming time series (STS). The high dimensionality and dynamic uncertainty of STS lead to the main challenge for similarity searching efficiency. In this paper, we propose an efficient searching approach for STS and our approach is more effective than traditional methods by utilizing the dimensionality reduction based representation and the optimized index on STS. © 2018 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063651756&doi=10.1109%2fICSESS.2018.8663834&partnerID=40&md5=57f3d6a8ddfa297b4763fa690a9df304
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