In the example, you used Hadoop to process Apache web server access logs.
在示例中，您使用Hadoop处理Apacheweb服务器访问日志。
The service provider analyzes its access logs and provides its customer with reports about what was requested and when.
服务提供商分析其访问日志，并为客户提供有关用户请求什么内容和在什么时间发出请求的报告。
You can change this configuration parameter during the roll-forward process to allow you to access logs in multiple locations.
在前滚过程中可以更改此配置参数以允许您访问多个位置中的日志。
For example, if you have filed your access logs using dates, you can then select all of the files within a specific range of dates.
例如，如果您已使用日期对访问日志归档，则可以选择在某个特定日期范围内的所有文件。
This paper presents a method for session identification based on an analysis of intervals of user access logs.
针对网络日志挖掘中的会话切分问题，提出了一种基于时间间隔的方法。
Experimental results show that the implementation can cluster the user access logs effectively, and the temporal and special efficiency can meet the on-the-fly need of Web server pre-fetching.
实验结果表明，这种实现能有效地对用户访问日志进行聚类，并能满足服务器预取机制在线分析的时间、空间要求。
Experimental results show that the implementation can cluster the user access logs effectively, and the temporal and special efficiency can meet the on-the-fly need of Web server pre-fetching.
实验结果表明，这种实现能有效地对用户访问日志进行聚类，并能满足服务器预取机制在线分析的时间、空间要求。
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