In this paper we investigate the effectiveness of ensemble-based learners for web robot session identification from web server logs. We also perform multi fold robot session labeling to improve the performance of lear...In this paper we investigate the effectiveness of ensemble-based learners for web robot session identification from web server logs. We also perform multi fold robot session labeling to improve the performance of learner. We conduct a comparative study for various ensemble methods (Bagging, Boosting, and Voting) with simple classifiers in perspective of classification. We also evaluate the effectiveness of these classifiers (both ensemble and simple) on five different data sets of varying session length. Presently the results of web server log analyzers are not very much reliable because the input log files are highly inflated by sessions of automated web traverse software’s, known as web robots. Presence of web robots access traffic entries in web server log repositories imposes a great challenge to extract any actionable and usable knowledge about browsing behavior of actual visitors. So web robots sessions need accurate and fast detection from web server log repositories to extract knowledge about genuine visitors and to produce correct results of log analyzers.展开更多
提出一个基于SQL Server 2005的Web日志挖掘解决方案,主要应用SSIS将日志数据从文本文件导入数据库,在SQL Server Management Studio中应用SQL语句和存储过程完成日志的预处理,然后应用SSAS完成数据挖掘任务,通过关联规则挖掘算法在Web...提出一个基于SQL Server 2005的Web日志挖掘解决方案,主要应用SSIS将日志数据从文本文件导入数据库,在SQL Server Management Studio中应用SQL语句和存储过程完成日志的预处理,然后应用SSAS完成数据挖掘任务,通过关联规则挖掘算法在Web日志的应用实例证明解决方案的有效性。展开更多
文摘In this paper we investigate the effectiveness of ensemble-based learners for web robot session identification from web server logs. We also perform multi fold robot session labeling to improve the performance of learner. We conduct a comparative study for various ensemble methods (Bagging, Boosting, and Voting) with simple classifiers in perspective of classification. We also evaluate the effectiveness of these classifiers (both ensemble and simple) on five different data sets of varying session length. Presently the results of web server log analyzers are not very much reliable because the input log files are highly inflated by sessions of automated web traverse software’s, known as web robots. Presence of web robots access traffic entries in web server log repositories imposes a great challenge to extract any actionable and usable knowledge about browsing behavior of actual visitors. So web robots sessions need accurate and fast detection from web server log repositories to extract knowledge about genuine visitors and to produce correct results of log analyzers.
文摘提出一个基于SQL Server 2005的Web日志挖掘解决方案,主要应用SSIS将日志数据从文本文件导入数据库,在SQL Server Management Studio中应用SQL语句和存储过程完成日志的预处理,然后应用SSAS完成数据挖掘任务,通过关联规则挖掘算法在Web日志的应用实例证明解决方案的有效性。
