R2Time: A Framework to Analyse Open TSDB Time-Series Data in HBase

B. Agrawal, Antorweep Chakravorty, Chunming Rong, T. Wlodarczyk
{"title":"R2Time: A Framework to Analyse Open TSDB Time-Series Data in HBase","authors":"B. Agrawal, Antorweep Chakravorty, Chunming Rong, T. Wlodarczyk","doi":"10.1109/CloudCom.2014.84","DOIUrl":null,"url":null,"abstract":"In recent years, the amount of time series data generated in different domains have grown consistently. Analyzing large time-series datasets coming from sensor networks, power grids, stock exchanges, social networks and cloud monitoring logs at a massive scale is one of the biggest challenges that data scientists are facing. Big data storage and processing frameworks provides an environment to handle the volume, velocity and frequency attributes associated with time-series data. We propose an efficient and distributed computing framework - R2Time for processing such data in the Hadoop environment. It integrates R with a distributed time-series database (Open TSDB) using a MapReduce programming framework (RHIPE). R2Time allows analysts to work on huge datasets from within a popular, well supported, and powerful analysis environment.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CloudCom.2014.84","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

In recent years, the amount of time series data generated in different domains have grown consistently. Analyzing large time-series datasets coming from sensor networks, power grids, stock exchanges, social networks and cloud monitoring logs at a massive scale is one of the biggest challenges that data scientists are facing. Big data storage and processing frameworks provides an environment to handle the volume, velocity and frequency attributes associated with time-series data. We propose an efficient and distributed computing framework - R2Time for processing such data in the Hadoop environment. It integrates R with a distributed time-series database (Open TSDB) using a MapReduce programming framework (RHIPE). R2Time allows analysts to work on huge datasets from within a popular, well supported, and powerful analysis environment.
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
R2Time:一个在HBase中分析开放TSDB时间序列数据的框架
近年来,不同领域产生的时间序列数据量持续增长。大规模分析来自传感器网络、电网、证券交易所、社交网络和云监控日志的大型时间序列数据集是数据科学家面临的最大挑战之一。大数据存储和处理框架提供了一个环境来处理与时间序列数据相关的数量、速度和频率属性。我们提出了一个高效的分布式计算框架——R2Time,用于在Hadoop环境中处理此类数据。它使用MapReduce编程框架(RHIPE)将R与分布式时间序列数据库(Open TSDB)集成在一起。R2Time允许分析人员在一个流行的、支持良好的、功能强大的分析环境中处理庞大的数据集。
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
Exploring the Performance Impact of Virtualization on an HPC Cloud Performance Study of Spindle, A Web Analytics Query Engine Implemented in Spark Role of System Modeling for Audit of QoS Provisioning in Cloud Services Dependability Analysis on Open Stack IaaS Cloud: Bug Anaysis and Fault Injection Delegated Access for Hadoop Clusters in the Cloud
×
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