Survey on incremental and iterative models in big data mining environment

Priyanka Joseph, J. C. Pamila
{"title":"Survey on incremental and iterative models in big data mining environment","authors":"Priyanka Joseph, J. C. Pamila","doi":"10.1109/ICACCS.2016.7586377","DOIUrl":null,"url":null,"abstract":"It has become increasingly popular to mine big data in order to gain insights to help business decisions or to provide more desirable personalized, higher quality services. They usually include data sets with sizes beyond the ability of commonly used software tools to retrieve, manage, and process data within an adequate elapsed time. So there is big demand for distributed computing framework. As new data and updates are constantly arriving, the results of data mining applications become incomplete over time. In such situations it is desirable to periodically refresh the mined data in order to keep it up-to-date. This paper describes the existing approaches to big data mining which uses these frameworks in an incremental approach that saves and reuses the previous states of computations. It also explores several enhancements introduced in this same framework with iterative mapping characteristics. Gaps in the current methods are identified in this literature review.","PeriodicalId":176803,"journal":{"name":"2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACCS.2016.7586377","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

It has become increasingly popular to mine big data in order to gain insights to help business decisions or to provide more desirable personalized, higher quality services. They usually include data sets with sizes beyond the ability of commonly used software tools to retrieve, manage, and process data within an adequate elapsed time. So there is big demand for distributed computing framework. As new data and updates are constantly arriving, the results of data mining applications become incomplete over time. In such situations it is desirable to periodically refresh the mined data in order to keep it up-to-date. This paper describes the existing approaches to big data mining which uses these frameworks in an incremental approach that saves and reuses the previous states of computations. It also explores several enhancements introduced in this same framework with iterative mapping characteristics. Gaps in the current methods are identified in this literature review.
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
大数据挖掘环境中增量与迭代模型研究综述
挖掘大数据以获得洞察力来帮助商业决策或提供更理想的个性化、更高质量的服务已经变得越来越流行。它们通常包括数据集,其大小超出了常用软件工具在足够的运行时间内检索、管理和处理数据的能力。因此对分布式计算框架的需求很大。随着新数据和更新的不断到来,数据挖掘应用程序的结果随着时间的推移变得不完整。在这种情况下,需要定期刷新挖掘的数据,以使其保持最新状态。本文描述了现有的大数据挖掘方法,这些方法以增量的方式使用这些框架来保存和重用以前的计算状态。本文还探讨了在这个框架中引入的几个具有迭代映射特征的增强。在这篇文献综述中确定了当前方法的差距。
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
Detection of selfish Nodes in MANET - a survey Robust Sybil attack detection mechanism for Social Networks - a survey A comparative study of DFT and Moving Window Averaging technique of current differential protection on Transmission line Online review analytics using word alignment model on Twitter data Hybrid cryptography mechanism for securing self-organized wireless networks
×
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