关于社交媒体流挖掘的见解

Rojina Deuja, Krishna Bikram Shah
{"title":"关于社交媒体流挖掘的见解","authors":"Rojina Deuja, Krishna Bikram Shah","doi":"10.3126/scitech.v14i1.25532","DOIUrl":null,"url":null,"abstract":"Data stream mining is one of the realms gaining upper hand over traditional data mining methods. Transfinite volumes of data termed as Data Streams are often generated by Internet traffic, Communication networks, On-line bank or ATM transactions etc. The streams are dynamic and ever-shifting and need to be analysed online as they are obtained. Social media is one of the notable sources of such data streams. While social media streaming has received a lot of attention over the past decade, the ever-expanding streams of data presents huge challenges for learning and maintaining control. Dealing with billions of user’s data measured in pet bytes is a demanding task in itself. It is indeed a challenge to mine such dynamic data from social networks in an uninterrupted and competent way. This paper is purposed to introduce social data streams and the mining techniques involved in processing them. We analyse the most recent trends in social media data stream mining to translate to the detailed study of the matter. We also review innovative implementations of social media stream mining that are currently prevalent.","PeriodicalId":183221,"journal":{"name":"SCITECH Nepal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Insight on Social Media Stream Mining\",\"authors\":\"Rojina Deuja, Krishna Bikram Shah\",\"doi\":\"10.3126/scitech.v14i1.25532\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data stream mining is one of the realms gaining upper hand over traditional data mining methods. Transfinite volumes of data termed as Data Streams are often generated by Internet traffic, Communication networks, On-line bank or ATM transactions etc. The streams are dynamic and ever-shifting and need to be analysed online as they are obtained. Social media is one of the notable sources of such data streams. While social media streaming has received a lot of attention over the past decade, the ever-expanding streams of data presents huge challenges for learning and maintaining control. Dealing with billions of user’s data measured in pet bytes is a demanding task in itself. It is indeed a challenge to mine such dynamic data from social networks in an uninterrupted and competent way. This paper is purposed to introduce social data streams and the mining techniques involved in processing them. We analyse the most recent trends in social media data stream mining to translate to the detailed study of the matter. We also review innovative implementations of social media stream mining that are currently prevalent.\",\"PeriodicalId\":183221,\"journal\":{\"name\":\"SCITECH Nepal\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SCITECH Nepal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3126/scitech.v14i1.25532\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SCITECH Nepal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3126/scitech.v14i1.25532","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

数据流挖掘是一个比传统数据挖掘方法更有优势的领域。被称为数据流的超量数据通常是由互联网流量、通信网络、网上银行或ATM交易等产生的。数据流是动态的,不断变化的,需要在获得数据后进行在线分析。社交媒体是这类数据流的重要来源之一。虽然社交媒体流在过去十年中受到了很多关注,但不断扩大的数据流给学习和保持控制带来了巨大的挑战。处理以pet字节为单位的数十亿用户数据本身就是一项艰巨的任务。从社交网络中以不间断和有效的方式挖掘这种动态数据确实是一项挑战。本文旨在介绍社会数据流以及处理这些数据流所涉及的挖掘技术。我们分析了社交媒体数据流挖掘的最新趋势,以转化为对此事的详细研究。我们还回顾了当前流行的社交媒体流挖掘的创新实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Insight on Social Media Stream Mining
Data stream mining is one of the realms gaining upper hand over traditional data mining methods. Transfinite volumes of data termed as Data Streams are often generated by Internet traffic, Communication networks, On-line bank or ATM transactions etc. The streams are dynamic and ever-shifting and need to be analysed online as they are obtained. Social media is one of the notable sources of such data streams. While social media streaming has received a lot of attention over the past decade, the ever-expanding streams of data presents huge challenges for learning and maintaining control. Dealing with billions of user’s data measured in pet bytes is a demanding task in itself. It is indeed a challenge to mine such dynamic data from social networks in an uninterrupted and competent way. This paper is purposed to introduce social data streams and the mining techniques involved in processing them. We analyse the most recent trends in social media data stream mining to translate to the detailed study of the matter. We also review innovative implementations of social media stream mining that are currently prevalent.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信