网络动态数据挖掘模式及其关键技术  

The Models and Key Technologies of Dynamic Network Data Mining

作　　者：黄晓斌[1] 张兴旺[1,2] 

机构地区：[1]中山大学资讯管理学院,广州510006 [2]桂林理工大学图书馆,桂林541004

出　　处：《图书情报工作》2015年第10期21-28,47,共9页Library and Information Service

基　　金：国家社会科学基金"网页内容分析与挖掘的企业竞争情报方法研究"(项目编号:10BTQ034)研究成果之一

摘　　要：[目的/意义]大数据蕴藏着大知识和大情报,但呈现出价值密度低、分布不规律、价值隐藏深、变化频繁等特征,给数据挖掘工作带来巨大机遇与挑战,有必要探索新的挖掘方式。[方法/过程]以大数据环境下网络动态数据挖掘模式作为切入点,从数据生产方式、生产规模及技术成熟性等维度选择金融管理、新型互联网应用、移动互联网位置服务等应用领域,对其基本特征进行分析,最后以Facebook的动态数据挖掘分析平台为例进行系统说明。[结果/结论]提出应从体系结构、动态数据传输、系统接口、高可用技术及负载均衡等关键技术入手,对系统进行设计与优化。[ Purpose/significance ] Big data contains big knowledge and big intelligence, but it also has the charac- teristics of low value density, irregular distribution, hidden value and frequent changes, which brings enormous opportuni- ties and challenges to data mining. It is necessary to explore new ways of mining. [ Method/process] Taking the network dynamic data mining mode in the big data environment as a starting point, selected the application areas of financial man- agement, the new Internet applications, location-based services, such as mobile Internet data, the basic characteristics were analyzed in terms of production, production scale and technical maturity. Finally, Facebook dynamic competitive in- telligence analysis platform was chosen to conduct a systematic analysis. [ Result/conclusion ] It proposes the architecture, the key technology of dynamic data transmission, system interface, high availability and load balancing technology and other aspects of the system design should be optimized.

关 键 词：大数据 动态数据挖掘 动态竞争情报 FACEBOOK 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] G250.7[自动化与计算机技术—计算机科学与技术]