服务器日志

无法更新服务器日志ip筛选器列表。

The server log IP filter list cannot be updated .

首先，根据对Web服务器日志数据格式的分析，对会话概念进行了形式化描述；

First of all , by the analysis of web server log format , we have given the formal descriptions of the concept of session .

Web服务器日志文件广义集成分析模型

A Generalized Integration Analysis Model of Web Logs

了解Web服务器日志的格式，以及如何用代码访问它们。

Learn about the format of Web server logs and how to access them in code .

有很多流行的工具可用于分析Web服务器日志和提供关于Web的统计信息。

There are many popular tools for analyzing Web server logs and presenting statistics on the Web .

Web服务器日志记录了用户与服务器的交互信息，反映了用户访问Web站点的所有动作。

Web server logs records the mutual reacting information between users and servers which reflects all movement by users .

其中，面向Web服务器日志的Web使用挖掘技术尤其得到了广大研究人员的关注。

Especially , lots of researchers pay more attentions to the Web usage mining which faces Web server logs .

另外，还应该启用DominoWeb服务器日志。

Additionally , enable Domino Web server logs .

本文首先系统的研究并提出了一种新的基于ServerSession的服务器日志记录格式。

This paper studies the system , and proposed a new based Server Session log format .

Web日志挖掘是Web数据挖掘的子领域，从Web服务器日志中提取感兴趣的知识模式。

Web log mining is that area of Web mining which deals with the extraction of interesting knowledge from logging information provided by Web servers .

通过对Web服务器日志文件和客户交易数据进行分析，可以发现相似客户群体、相关Web页面和频繁访问路径。

Similar customer groups , relevant Web pages , and frequent access paths can be discovered by analyzing of Web log files and customer database .

图书馆Apache服务器日志文件数据的分析

The Analysis of the Data in the Library Apache Server Log File

算法可以从Web服务器日志中挖掘出用户信息和数据信息，有效地识别用户访问模式。

The algorithm can extract the information of the user and data from Web sever log , and can identify the user 's access path efficiently .

前者是对web服务器日志进行数据挖掘分析，后者是通过特定工具对用户的访问模式进行挖掘分析，从而得到用户使用web的规律。

Web log mining is data mining analysis for a web server logs . The custom mining is analysis of user rule according to specific tools .

检查WebSphere服务器日志。

Check the WebSphere server logs .

在算法研究的基础上，本文进一步详细介绍了系统的Web日志预处理模块、基本分析模块、模式发现模块的设计与实现，并使用该系统对真实的服务器日志文件进行挖掘，给出了分析结果。

Base on the studies of all above , this paper expatiated design and implementation of data preprocessing block , basic analysis block and model discovery block in detail .

reduce过程的结果是基于Web服务器日志的给定Web站点的每次URL访问的总数。

The result of the reduce process is the total number of accesses per URL for a given Web site based on the Web server logs .

从数量化角度给出了异常数据的一般性定义，以Web服务器日志文件数据为依据，讨论了挖掘异常数据的方法和途径；

This paper proposes the general definition of outlier data based on quantity , and then discusses the methods of mining outlier data on the basis of Web server log .

该算法通过利用会话机制、IP地址和访问时间来准确识别服务器日志中的用户。

The algorithm is used to accurately identify users in the server logs by using the session mechanism , IP address and access time to .

日常商业运作中，电子商务网站会产生大量的商业数据，商务智能领域研究的任务之一就包括从Web服务器日志中挖掘出知识集。

E-commerce Web sites often generate large volumes of data in their daily operations . Analyzing such data involves the discovery of meaningful relationships from access logs stored in Web server .

除了这一点，通过日志您要试着确认，当您使用Web服务时程序服务器日志目录中都有什么内容。

In addition to this , try to verify , through the logs , what is getting posted in the application server logs directory when you are using the web service .

为了解决上述问题，Web挖掘技术应运而生，其中，面向Web服务器日志的Web日志挖掘技术尤其得到了众多研究人员的关注。

In order to solve the problem mentioned above , Web data mining emerges as the times require . Thereinto , the Web log mining technology is paid more attentions by numerous researchers especially .

Web日志挖掘就是运用数据挖掘的思想来对服务器日志进行分析处理，从而解决上面提出的各种问题。

Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above .

该文提出了基于Web本体和服务器日志文件的知识发现模型，主要讨论了用户访问行为的表示、语义用户分布的定义及发现算法。

This paper proposes a new model for knowledge discovery based on web log files and ontology , chiefly discusses the presentation of user access behaviors and the definition and discovery algorithm of semantic user profiles .

作为特例，把单指标的异常数据挖掘算法应用于校园网Web服务器日志文件，给出了上网用户的频率分析图。

As for special example , the single-criterion algorithm for mining outlier data based on the distance was applied to the Web service log in campus networks . The frequency analysis chart including outlier data sign was presented .

按照标准的服务器日志格式，对图书馆Web服务器日志文件的记录进行分析，并通过对其一定时间段的数据挖掘，对图书馆网站的使用状况进行了有益的探讨。

This paper analyses the data of the log file in the library apache server based on the standard format of server log file . With the data mining technology , the using status of the library Website are studied .

CommunityEdition服务器日志可以在installDir/var/log目录中找到，其中installDir是服务器的安装目录。

Community Edition server logs can be found in the installDir / var / log directory where installDir is the server 's installation directory .

本文借助中国互联网络信息中心负责管理的国家域名系统资源，采用了若干CN节点的DNS服务器日志数据，对互联网访问模式进行了分析。

These data was provided by the China Internet Network Information Center , which is responsible for the management of national domain name resources .

Web使用挖掘是应用于Internet的技术，Internet中的数据是半结构化的，很难对它进行处理，但是Web服务器日志记录具有良好的结构，非常有利于数据挖掘的进行。

The web usage mining is based on internet . Data in internet is half structure and it is difficult to deal with . Fortunately , the web server log files have a nice structure and it is convenient for data mining .

甚至可以把这两个服务器日志记录到syslog，而不是直接写到文件。

Both the server logs can even be logged to syslog instead of directly to a file .