数据挖掘技术在Web预取中的应用研究

WWW以其多媒体的传输及良好的交互性而倍受青睐,虽然近几年来网络速度得到了很大的提高,但是由于接入Internet的用户数量剧增以及Web服务和网络固有的延迟,使得网络越来越拥护,用户的服务质量得不到很好的保证。为此文中提出了一种智能Web预取技术,它能够加快用户浏览Web页面时获取页面的速度。该技术通过简化的WWW数据模型表示用户浏览器缓冲器中的数据,在此基础上利用数据挖掘技术挖掘用户的兴趣关联规则,存放在兴趣关联知识库中,作为对用户行为进行预测的依据。在用户端,智能代理负责用户兴趣的挖掘及基于兴趣关联知识库的Web预取,从而对用户实现透明的浏览器加速。     

How Internet Users' Privacy Concerns Have Evolved since 2002

In 2002, the authors established a baseline for Internet users' online privacy values, finding information transfer, notice/awareness, and information storage were the top online privacy concerns. Since this survey, many privacy-related events have occurred, including changes in online trends and the creation of laws, prompting the authors to rerun the survey in 2008 to examine how these events might have affected users' online privacy concerns. In this article, they discuss the 2008 survey, which revealed that US Internet users' top three privacy concerns didn't change over the course of six years, although their level of concern did. The authors also examine differences in privacy concerns between US and international respondents. The Web extra groups the survey statements according to six dimensions of privacy concerns based on the following classifications—personalization, notice/awareness, information transfer, information collection, information storage, and access/participation.     

网络号百用户兴趣模型挖掘算法

为了实现在线推荐信息服务,要对网络号百用户的访问行为进行分析,荻取用户访问聚类模型,从而在聚类模型的基础上进行在线推荐.介绍获取用户访问路径信息的方法,对用户访问路径信息建立相似度矩阵,基于相似度矩阵改进K-means算法,据此进行用户模型聚类,给出分析案例,并说明算法实现过程.     

基于代理的互联网用户行为分析研究

在互联网智能化的过程中,互联网用户行为的分析是一个必要的工作.通过架设网络代理,记录用户在互联网上发出的HTTP请求,建立用户行为日志库,并根据Web访问的特性对用户行为日志进行过滤、聚类,缩减数据规模,最后利用开放式分类目录ODP(Open Directory Project)对用户行为进行分类统计,将没有语义信息的...     

分布式开放存取期刊爬虫的设计与实现

开放存取（open access,OA）期刊属于网络深层资源且分散在互联网中,传统的搜索引擎不能对其建立索引,不能满足用户获取OA期刊资源的需求,从而造成了开放资源的浪费。针对如何集中采集万维网上分散的开放存取期刊资源的问题,提出了一个面向OA期刊的分布式主题爬虫架构。该架构采用主从分布式设计,提出了基于用户预定义规则的OA期刊页面学术信息提取方法,由一个主控中心节点控制多个可动态增减的爬行节点,采用基于Chrome浏览器的插件机制来实现分布式爬行节点的可扩展性和部署的灵活性。     

基于用户日志挖掘的搜索引擎广告效果分析

随着搜索引擎市场的飞速发展,竞价排名广告以其有效、低风险、灵活等特点逐渐受到中小企业用户的青睐,成为搜索引擎稳定的收益增长点。然而竞价排名广告是否会影响用户体验,从而削弱其宣传效果并且影响用户对于搜索引擎的忠实度成为了企业及搜索引擎所担忧的问题。该文从网络用户日志中挖掘出网络用户对于广告的实际交互行为,并给出了各大搜索引擎竞价排名广告方面的统计数据。对于企业用户如何更有效地利用竞价排名广告以及搜索引擎如何平衡广告的经济效益和用户体验之间的关系都有较高的指导意义。     

WebCompanion: a friendly client-side Web prefetching agent

Accessing remote sites of the World Wide Web is often a frustrating experience for users because of long Web page retrieval times even over relatively fast Internet connections. Users are more likely to embrace the further expansion of the role of the Web into a major infrastructure for electronic commerce and for information, application, and multimedia delivery if Web accesses can be accelerated. One technique that attempts this is prefetching. We built a client-side Java-implemented prefetching agent, WebCompanion, which employs a novel adaptive, fast, and selective online prefetching strategy based on estimated round-trip times for Web resources. This strategy efficiently hides the access latencies for slow resources while at the same time limiting the network and server overhead and local resource consumption to moderate levels. Our extensive experiments show an average access speedup of greater than 50 percent and an average network byte overhead of less than 150 percent using WebCompanion over a fast Internet connection. We measured a slight acceleration in accessing the Web through WebCompanion even in a pessimistic scenario where the user never requests a prefetched document     

Discovering users with similar internet access performance through cluster analysis

Users typically subscribe to an Internet access service on the basis of a specific download speed, but the actual service may differ. Several projects are active collecting internet access performance measurements on a large scale at the end user location. However, less attention has been devoted to analyzing such data and to inform users on the received services. This paper presents MiND, a cluster-based methodology to analyze the characteristics of periodic Internet measurements collected at the end user location. MiND allows to discover (i) groups of users with a similar Internet access behavior and (ii) the (few) users with somehow anomalous service. User measurements over time have been modeled through histograms and then analyzed through a new two-level clustering strategy. MiNDhas been evaluated on real data collected by Neubot, an open source tool, voluntary installed by users, that periodically collects Internet measurements. Experimental results show that the majority of users can be grouped into homogeneous and cohesive clusters according to the Internet access service that they receive in practice, while a few users receiving anomalous services are correctly identified as outliers. Both users and ISPs can benefit from such information: users can constantly monitor the ISP offered service, whereas ISPs can quickly identify anomalous behaviors in their offered services and act accordingly.     

Real geo-time-based secured access computation model for e-Health systems

Role Back Access Control model (RBAC) allows devices to access cloud services after authentication of requests. However, it does not give priority in Big Data to devices located in certain geolocations. Regarding the crisis in a specific region, RBAC did not provide a facility to give priority access to such geolocations. In this paper, we planned to incorporate Location Time- (GEOTime) based condition alongside Priority Attribute role-based access control model (PARBAC), so requesters can be allowed/prevented from access based on their location and time. The priority concept helped to improve the performance of the existing access model. TIME-PARBAC also ensures service priorities based on geographical condition. For this purpose, the session is encrypted using a secret key. The secret key is created by mapping location, time, speed, acceleration and other information into a unique number, that is, K(Unique_Value) = location, time, speed, accelerator, other information. Spatial entities are used to model objects, user position, and geographically bounded roles. The role is activated based on the position and attributes of the user. To enhance usability and flexibility, we designed a role schema to include the name of the role and the type of role associated with the logical position and the rest of the PARBAC model proposed using official documentation available on the website for Azure internet of things (IoT) Cloud. The implementation results utilizing a health use case signified the importance of geology, time, priority and attribute parameters with supporting features to improve the flexibility of the existing access control model in the IoT Cloud.     

一种基于QoS的Internet服务管理体系结构

服务定位协议(SLP)是IETF提出的基于IP网络的服务定位标准。而这一标准并没有考虑对QoS的支持。未来的互联网络是一个具有QoS保证的网络。服务发现仅仅是Internet服务管理中的一个方面，发现服务不是最终目的，最终目的是为了使用这一服务。在对SLP体系结构、下一代互联网QoS服务管理及相关技术充分研究的基础上，提出了一种新奇的基于QoS的Internet服务管理体系结构模型。为使在服务表示与发现层支持QoS，对SLP定位协议进行了一些扩充。对服务定位协议扩充后，当用户代理在向服务代理发布请求时，可以在请求消息中绑定QoS对象参数，一旦服务代理接收到这一消息，就可以利用这些QoS参数与域管理器进行协商，协商结果可通过服务代理反馈给用户代理。这样不仅减小了网络开销，而且降低了传统的用户代理与域管理器协商的次数，在服务发现的同时用户代理还可以直接获得该服务的QoS信息。     

一种基于位置数据库聚类的动态适应缓存位置信息策略

移动环境中提高定位移动用户性能的一个重要方法是缓存用户的位置信息,然而已经提出的缓存策略针对的是单个用户,造成缓存的效率不高,针对群体用户提出了一种基于位置数据库聚类的动态适应缓存位置信息(DACaL)策略,其中位置数据库聚类算法通过挖掘群体移动用户的运动模式对位置数据库进行聚类,以确定缓存层次和降低位置管理的代价,动态适应缓存位置信息算法根据聚类结果对位置数据库进行重组,在相邻聚类之间缓存位置信息,建立旁路指针,以缩短消息传输的路径和减少查询位置数据库的次数,实验表明,DACaL策略能够有效地降低总体代价,性能上优于相关策略,     

Design and implementations of Ninf: towards a global computing infrastructure

The world-wide computing infrastructure on the growing computer network technology is a leading technology to make a variety of information services accessible through the Internet for every user from the high-performance computing users through many of personal computing users. The important feature of such services is location transparency; information can be obtained irrespective of time or location in virtually shared manner. In this article, we overview Ninf, an ongoing global network-wide computing infrastructure project which allows users to access computational resources including hardware, software and scientific data distributed across a wide area network. Preliminary performance result on measuring software and network overhead is shown, and that promises the future reality of world-wide network computing.     

在线草图识别中用户手绘习惯建模方法

手绘草图是概念设计和思路外化的一种高效的表达方式。用户绘制草图时存在的多种形式，及其随意性和模糊性使得用户适应性问题逐渐成为草图识别的核心课题。本文提出了一种在线草图识别的用户建模方法来捕捉绘制草图时的用户习惯，主要包括两个方面的内容：一是基于SVM的主动式增量学习方法，二是基于动态用户建模的手绘复杂图形的识别方法。前者与传统的增量式学习方法相比，在识别精度相同的情况下所需的训练时间和训练数据集要少得多。后者则是基于笔划信息以及笔划间的顺序和空间关系信息，采用增量式决策树捕捉用户的输入习惯和过程信息。实验证明了本文方法在在线草图识别中的有效性和高效性。     

Digital content recommender on the Internet

On the Web, where information is vast and users are numerous, personalization that aims to offer suitable information to suitable users is essential. To sustain their competitive advantage, portal sites attract many users' attention by supplying personalized content. Most Web content providers offer all users the same content, failing to satisfy individual users' needs. Providers should be able to offer suitable users suitable content with suitable speed. To do so, they must be able to identify customers, predict their interests, determine appropriate content, and deliver it in a personalized format during customers' online sessions. In this paper, the author presents a digital-content recommender system that suggests Web content, in this case news articles, based on a user's preference when he or she visits an Internet news site and reads the published articles. This recommender system creates a one-to-one relationship between the content provider and the user, raises the user's satisfaction, and increases loyalty toward the content provider.     

利用ActiveX技术的Web-enabled C/S地理信息系统的设计和实现

Internet是全球性的计算机网络,它改变了传统的数据访问、数据共享和数据分发方式,在地理信息系统（GeographicInformationSystem）中,它甚至改变了信息的表现和分析手段。目前,基于Internet的地理信息系统正随着Internet和WWW相关技术的发展而讯速发展着。该文利用ActiveX技术实现了一个基于Internet的、Client／Server模式的地理信息系统,该系统基本提供了传统桌面GIS软件包的全部功能。用户可以用普通测览器（如InternetExplorer等）,象使用传统桌面GIS软件一样,在特定HTML网页中测览和操作存储在远端GIS服务器中的地图数据及关联信息。     

网站浏览路径预测

用户浏览因特网网站点击某个链接后,该链接的网页往往需要一段时间才能传送到用户端.为了缩短用户的等待时间,利用网站服务器的空闲CPU时间,对用户下一个HTTP请求进行预测,以便预处理下一个网页是一种可能途径.使用已分类的网页信息、用户配置文件和网站日志进行预测,介绍并且分析了16种预测算法.实验结果证明某些算法是比较有效可信的.     

虚拟空间中社会分层行为研究

大量的人类行为发生在互联网上,互联网已成为与真实空间相对应的最重要的虚拟空间。传统虚拟空间中的社会分层研究基于网络信息资源占有的机会和能力等客观指标,并未涉及用户使用网络资源的具体行为及信息的内容和性质等因素。利用中国互联网络信息中心提供的用户在线行为大数据,从在线时间和上网内容两方面考察并分析了不同阶层的用户在虚拟空间中上网行为的特征和差异性。研究发现不同阶层的用户在虚拟空间中的停留时间和注意力聚焦点都大不相同。较高阶层用户能更好地利用网络资源办公和购物,且在虚拟空间中的停留时间具有相对稳定性。而较低阶层用户将大量的注意力消耗在休闲娱乐类应用上,且停留时间不稳定。此外,本文利用基于word2vec的神经网络模型(W2V-BP),对用户在虚拟空间中的上网行为数据进行社会分层识别,识别准确率达到90.22%,表明虚拟空间中存在能够区分社会分层的行为特征。     

Agent在Internet中的应用

随着Internet自身的快速增长,在它之上的信息已经使得用户可以无限地进行文档访问,但同时也产生了用户找不到感兴趣文档的困难。我们提出了一种Agent系统模型,它可以自动学习用户的访问模式和信息资源的更新模式,将信息主动地推到用户面前,从而最大可能地满足了用户方的需求和减轻了服务方的开销。     

基于网络性能的智能Web加速技术——缓存与预取

Web业务在网络业务中占有很大比重,在无法扩大网络带宽时,需要采取一定技术合理利用带宽,改善网络性能。研究了基于RTT（round trip time)等网络性能指标的Web智能加速技术,在对Web代理服务器上的业务进行分析和对网络RTT进行测量分析的基础上,提出了智能预取控制技术及新的缓存（cache）替换方法。对新算法的仿真研究表明,该方法提高了缓存的命中率。研究表明预取技术在不明显增加网络负荷的前提下,提高了业务的响应速度,有效地改进了Web访问性能。     

Time-Aware Web Users' Clustering

Web users' clustering is a crucial task for mining information related to users' needs and preferences. Up to now, popular clustering approaches build clusters based on usage patterns derived from users' page preferences. This paper emphasizes the need to discover similarities in users' accessing behavior with respect to the time locality of their navigational acts. In this context, we present two time-aware clustering approaches for tuning and binding the page and time visiting criteria. The two tracks of the proposed algorithms define clusters with users that show similar visiting behavior at the same time period, by varying the priority given to page or time visiting. The proposed algorithms are evaluated using both synthetic and real data sets and the experimentation has shown that the new clustering schemes result in enriched clusters compared to those created by the conventional non-time-aware user clustering approaches. These clusters contain users exhibiting similar access behavior in terms not only of their page preferences but also of their access time.