一种新的Web频繁访问模式挖掘算法

提出了一种基于有向图的从Web日志中挖掘用户频繁访问模式的新算法,与传统使用基于关联规则挖掘的序列模式挖掘技术相比,本算法采用有向图来记录Web访问序列和它的计数,在挖掘过程中只需要扫描数据库一次,不产生数量庞大的候选模式,即可直接挖掘出所有的Web频繁访问路径,大大提高了Web访问模式的发现效率。     

无线Web访问的缓存和预取机制

缓存和预取在提高无线环境下的Web访问性能方面发挥着重要作用。文章研究针对无线局域网的Web缓存和预取机制,分别基于数据挖掘和信息论提出了采用序列挖掘和延迟更新的预测算法,设计了上下文感知的预取算法和获益驱动的缓存替换机制,上述算法已在Web缓存系统OnceEasyCache中实现。性能评估实验结果表明,上述算法的集成能有效地提高缓存命中率和延迟节省率。     

Web访问序列模式挖掘算法的研究

针对现有Web访问序列模式挖掘算法和PrefixSpan算法存在的问题,提出一种基于投影位置的Web访问序列模式挖掘算法(PWSPM)。该算法通过序列模式分析,发现用户的行为模式,预测用户对网页的访问模式,进而改进站点的性能和组织结构,提高用户查找信息的质量和效率,以及对用户开展个性化的信息服务。实验和应用结果表明,提出的算法具有更好的执行效率,适用于Web日志挖掘,可用于构建智能化Web站点和解决个性化的信息服务问题。     

用户Web日志序列模式挖掘研究

单个用户访问网站能够留下大量的访问信息，合理地挖掘这些信息便能够得到用户个人的访问模式。文中将序列模式挖掘运用到单一用户Web日志上．最终可以得到单一用户的访问序列模式。在序列模式挖掘过程中，将传统的序列模式挖掘概念进行了扩充，对应到单一用户的序列模式上；运用Session来划分时间段，增强了时间的概念；运用概念格的理论，很好地实现了增量序列模式挖掘。并使用一个新的算法，解决MFP(最大前向路径)在Web日志中获取存在的一些问题。     

用户Web 日志序列模式挖掘研究

单个用户访问网站能够留下大量的访问信息,合理地挖掘这些信息便能够得到用户个人的访问模式.文中将序列模式挖掘运用到单一用户Web日志上,最终可以得到单一用户的访问序列模式.在序列模式挖掘过程中,将传统的序列模式挖掘概念进行了扩充,对应到单一用户的序列模式上;运用Session来划分时间段,增强了时间的概念;运用概念格的理论,很好地实现了增量序列模式挖掘.并使用一个新的算法,解决MFP(最大前向路径)在Web日志中获取存在的一些问题.     

基于完井业务流程的加权序列模式挖掘算法研究

针对完井移动平台Web访问模式中用户对整体上符合完井业务流程习惯的序列模式更加感兴趣的特点,提出一种基于完井业务流程的加权序列模式挖掘算法.通过对完井业务流程模型和完井Web访问日志作分析,确定完井业务依赖强度计算模型并作为序列的权重,同时在加权挖掘的过程中采用k-最小加权支持数技术,对PrefixSpan算法进行改进.该算法在加权挖掘的过程中可以舍弃一大批不满足业务流程的访问序列,有效地避免候选组合爆炸的发生.实验表明该算法能快速地挖掘出有意义的完井访问序列模式.     

基于网页结构相关性的隐马尔可夫预取技术研究

基于网页的结构相关性及隐马尔可夫过程提出一种基于Web网页结构相关性的预取模型,通过网页抓捕建立特征词集,在此特征词集的基础上对用户的访问序列进行分析,提取超链接语义中蕴含的信息需求,在此特征词集的基础上对网页超链语义进行分析。模型引入隐马尔可夫模型实现用户访问序列中潜在意图的挖掘。性能测试实验的结果表明,该模型具有较好的整体性能。     

基于过滤器的Web访问模式挖掘

针对传统Web访问模式挖掘系统中用户识别和会话识别的复杂性和不准确性，该文提出了基于过滤器的Web访问模式挖掘系统。它能够准确地识别用户和会话，为挖掘算法提供优质的数据。给出了日志过滤器的实现和部署，提出了Web访问模式的挖掘算法。目前该方法已经广泛地应用于科学数据库系统中。     

挖掘基于Web的访问路径模式

本文介绍了一种新的基于Web的序列模式－访问路径模式挖掘问题,给出了问题的形式化描述以及挖掘访问路径模式的方法,提出了识别最大前向访问路径和发现大访问路径的算法。     

基于用户访问模式的Web预取算法

缩短Web访问中的用户感知时间,是Web应用中的一个重要问题,服务器需要预测用户未来的HTTP请求和处理当前的网页以提高Web服务器的响应速度,为此提出了一种基于用户访问模式的Web预取算法.该算法根据Web日志信息分析了用户的访问模式,并计算出Web页面间的转移概率,以此作为对用户未来请求预取的依据.实验结果表明,该预取算法能有效提高预测精度和命中率,有效地缩短了用户的感知时间.     

A clustering-based prefetching scheme on a Web cache environment

Web prefetching is an attractive solution to reduce the network resources consumed by Web services as well as the access latencies perceived by Web users. Unlike Web caching, which exploits the temporal locality, Web prefetching utilizes the spatial locality of Web objects. Specifically, Web prefetching fetches objects that are likely to be accessed in the near future and stores them in advance. In this context, a sophisticated combination of these two techniques may cause significant improvements on the performance of the Web infrastructure. Considering that there have been several caching policies proposed in the past, the challenge is to extend them by using data mining techniques. In this paper, we present a clustering-based prefetching scheme where a graph-based clustering algorithm identifies clusters of “correlated” Web pages based on the users’ access patterns. This scheme can be integrated easily into a Web proxy server, improving its performance. Through a simulation environment, using a real data set, we show that the proposed integrated framework is robust and effective in improving the performance of the Web caching environment.     

一种智能的预取算法

网络延迟问题是用户QoS的主要问题之一,它依赖诸多因素如网络带宽、传输延迟、排队延迟和客户机及服务器的处理速度。目前主要采用缓存和预取技术来减少网络延迟,但缓存技术所提高的缓存代理服务器的命中率是有限的。该文系统地阐述了目前预取算法的基本思想并把它们分成四类:基于流行度、基于交互、基于访问概率和基于数据挖掘的预取算法。在对它们进行分析比较的基础上,提出了一种智能的预取方案。该方案使用模糊匹配来计算用户对页面的访问概率,同时要控制预取的量和预取的时刻,以避免对网络的性能产生负面影响。     

Integrating Web Prefetching and Caching Using Prediction Models

Web caching and prefetching have been studied in the past separately. In this paper, we present an integrated architecture for Web object caching and prefetching. Our goal is to design a prefetching system that can work with an existing Web caching system in a seamless manner. In this integrated architecture, a certain amount of caching space is reserved for prefetching. To empower the prefetching engine, a Web-object prediction model is built by mining the frequent paths from past Web log data. We show that the integrated architecture improves the performance over Web caching alone, and present our analysis on the tradeoff between the reduced latency and the potential increase in network load.     

一种基于预取的集群服务器调度算法

针对集群服务器LARD调度算法只能利用已有缓存的问题,提出一种基于预取的算法Prefetch-LARD,该算法从Web访问日志中挖掘页面之间的转移概率,建立马尔科夫链模型,在调度请求时利用概率关系提前将下一次可能访问的文档从节点磁盘取到本地cache中,提高了请求的缓存命中率;算法还采用了加权的节点超载判断方法,以提高集群节点的负载均衡度,实验表明,在同样的测试环境下,Prefetch-LARD算法比LARD算法的缓存命中率提高26．9％,系统的吞吐量相应提高18．8％．     

Web预取技术的研究

预取技术是提高缓存命中率和解决Web访问延迟问题的主要方案,本文研究了网页预取技术,将数据挖掘应用于Web预取中,设计了一个为用户提供个性化服务的Web预取模型;详细介绍了对Web日志进行预处理的方法;提出了新的预取替换算法。     

一种基于图结构挖掘WEB用户访问模式的方法

挖掘Web用户访问模式常用的技术有Web挖掘特有的路径分析技术和数据挖掘领域的传统技术。文章首先分析了现有路径分析技术的不足,然后从Web用户访问模式挖掘过程预处理的结果用户会话文件开始,提出了一种基于Web拓扑结构(图结构)挖掘用户访问模式的方法,提高了发现模式的精确性和效率,并在实验室对该方法进行了简单实现和实际日志数据的测试。     

A Data Cube Model for Prediction-Based Web Prefetching

Reducing the web latency is one of the primary concerns of Internet research. Web caching and web prefetching are two effective techniques to latency reduction. A primary method for intelligent prefetching is to rank potential web documents based on prediction models that are trained on the past web server and proxy server log data, and to prefetch the highly ranked objects. For this method to work well, the prediction model must be updated constantly, and different queries must be answered efficiently. In this paper we present a data-cube model to represent Web access sessions for data mining for supporting the prediction model construction. The cube model organizes session data into three dimensions. With the data cube in place, we apply efficient data mining algorithms for clustering and correlation analysis. As a result of the analysis, the web page clusters can then be used to guide the prefetching system. In this paper, we propose an integrated web-caching and web-prefetching model, where the issues of prefetching aggressiveness, replacement policy and increased network traffic are addressed together in an integrated framework. The core of our integrated solution is a prediction model based on statistical correlation between web objects. This model can be frequently updated by querying the data cube of web server logs. This integrated data cube and prediction based prefetching framework represents a first such effort in our knowledge.     

Web user clustering and Web prefetching using Random Indexing with weight functions

Users of a Web site usually perform their interest-oriented actions by clicking or visiting Web pages, which are traced in access log files. Clustering Web user access patterns may capture common user interests to a Web site, and in turn, build user profiles for advanced Web applications, such as Web caching and prefetching. The conventional Web usage mining techniques for clustering Web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users?? navigational behaviour. In this paper, we propose an approach based on a vector space model, called Random Indexing, to discover such intrinsic characteristics of Web users?? activities. The underlying factors are then utilised for clustering individual user navigational patterns and creating common user profiles. The clustering results will be used to predict and prefetch Web requests for grouped users. We demonstrate the usability and superiority of the proposed Web user clustering approach through experiments on a real Web log file. The clustering and prefetching tasks are evaluated by comparison with previous studies demonstrating better clustering performance and higher prefetching accuracy.