Research issues in mining multiple data streams

Wenyan Wu, L. Gruenwald
{"title":"Research issues in mining multiple data streams","authors":"Wenyan Wu, L. Gruenwald","doi":"10.1145/1833280.1833288","DOIUrl":null,"url":null,"abstract":"There exist emerging applications of data streams that have mining requirements. Although single data stream mining has been extensively studied, little research has been done for mining multiple data streams (MDS), which are more complex than single data streams and involved in many real-world applications. This paper discusses the characteristics of MDS, proposes a formal definition for them, analyzes MDS application in terms of mining requirements, and identifies research issues for MDS mining.","PeriodicalId":383372,"journal":{"name":"StreamKDD '10","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"StreamKDD '10","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1833280.1833288","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27

Abstract

There exist emerging applications of data streams that have mining requirements. Although single data stream mining has been extensively studied, little research has been done for mining multiple data streams (MDS), which are more complex than single data streams and involved in many real-world applications. This paper discusses the characteristics of MDS, proposes a formal definition for them, analyzes MDS application in terms of mining requirements, and identifies research issues for MDS mining.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
多数据流挖掘的研究问题
存在具有挖掘需求的数据流的新兴应用。虽然单数据流挖掘已经得到了广泛的研究,但对多数据流(MDS)的挖掘研究很少,MDS比单数据流更复杂,涉及许多实际应用。本文讨论了MDS的特点,提出了MDS的形式化定义,从挖掘需求的角度分析了MDS的应用,提出了MDS挖掘的研究问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Fully decentralized computation of aggregates over data streams CALDS: context-aware learning from data streams Towards subspace clustering on dynamic data: an incremental version of PreDeCon Research issues in mining multiple data streams Conformal prediction for distribution-independent anomaly detection in streaming vessel data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1