标题:Diversified Representation Approach for Time Series Using Storm
作者:Luo, Wei ;Zhang, Qi ;Ji, Cun ;Zhan, Peng ;Zheng, Jiecai ;Li, Xueqing
通讯作者:Ji, Cun
作者机构:[Luo, Wei ;Zhang, Qi ;Zhan, Peng ;Li, Xueqing ] School of Software, Shandong University, Jinan, China;[Zheng, Jiecai ] School of Sport Communication a 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:757-761
DOI:10.1109/ICSESS.2018.8663871
关键词:Data Mining; Diversified representation; Online segmentation; Streaming time series
摘要:With the burgeoning of IoE (Internet of Everything), massive numbers of IoT 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 on traditional time series data mining research. Accordingly, time series representation methods could not only reduce the original high dimensionality of streaming time series, but also contain the main temporal features of raw time series. More importantly, time series representation has been regarded as an necessary preprocessing tool to provide data support for the smooth progress of follow-up research. In this paper, we propose a novel online time series representation approach called continuous segmentation and diversified representation framework (CSDRF) for streaming time series, which contains two different types of time series representation results. The subsequent experiments have been conducted to demonstrate that CSDRF could not only provide the corresponding results to meet the diverse needs of different users, but also provide the corresponding qualified symbolic representation results for time series clustering. © 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-s2.0-85063620945&doi=10.1109%2fICSESS.2018.8663871&partnerID=40&md5=6305706a65022cdc6a1cc847b2f934f3
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