Management of Distributed Big Data for Social Networks

C. Leung, Hao Zhang
{"title":"Management of Distributed Big Data for Social Networks","authors":"C. Leung, Hao Zhang","doi":"10.1109/CCGrid.2016.107","DOIUrl":null,"url":null,"abstract":"In the current era of Big Data, high volumes of a wide variety of valuable data can be easily collected and generated from a broad range of data sources of different veracities at a high velocity. Due to the well-known 5V's of these Big Data, many traditional data management approaches may not be suitable for handling the Big Data. Over the past few years, several applications and systems have developed to use cluster, cloud or grid computing to manage Big Data so as to support data science, Big Data analytics, as well as knowledge discovery and data mining. In this paper, we focus on distributed Big Data management. Specifically, we present our method for Big Data representation and management of distributed Big Data from social networks. We represent such big graph data in distributed settings so as to support big data mining of frequently occurring patterns from social networks.","PeriodicalId":103641,"journal":{"name":"2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCGrid.2016.107","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15

Abstract

In the current era of Big Data, high volumes of a wide variety of valuable data can be easily collected and generated from a broad range of data sources of different veracities at a high velocity. Due to the well-known 5V's of these Big Data, many traditional data management approaches may not be suitable for handling the Big Data. Over the past few years, several applications and systems have developed to use cluster, cloud or grid computing to manage Big Data so as to support data science, Big Data analytics, as well as knowledge discovery and data mining. In this paper, we focus on distributed Big Data management. Specifically, we present our method for Big Data representation and management of distributed Big Data from social networks. We represent such big graph data in distributed settings so as to support big data mining of frequently occurring patterns from social networks.
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
面向社交网络的分布式大数据管理
在当前的大数据时代,可以很容易地从不同真实性的广泛数据源中以高速度收集和生成大量、种类繁多的有价值数据。由于这些大数据众所周知的5V,许多传统的数据管理方法可能不适合处理大数据。在过去的几年中,已经开发了一些应用程序和系统来使用集群,云或网格计算来管理大数据,以支持数据科学,大数据分析以及知识发现和数据挖掘。本文主要研究分布式大数据管理。具体来说,我们提出了大数据表示和管理来自社交网络的分布式大数据的方法。我们将这种大图数据在分布式环境中表示出来,从而支持对社交网络中频繁出现的模式进行大数据挖掘。
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
Increasing the Performance of Data Centers by Combining Remote GPU Virtualization with Slurm DiBA: Distributed Power Budget Allocation for Large-Scale Computing Clusters Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era DTStorage: Dynamic Tape-Based Storage for Cost-Effective and Highly-Available Streaming Service Facilitating the Execution of HPC Workloads in Colombia through the Integration of a Private IaaS and a Scientific PaaS/SaaS Marketplace
×
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