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
针对由Co De Sys Soft Motion控制的六自由度工业机器人,设计了一种基于OPC标准协议和Web技术的工业机器人3D(三维)虚拟动态监控系统。基于.NET平台的Windows窗体应用程序框架和My SQL数据库,采用C#语言设计了工业机器人监控客户端程序...针对由Co De Sys Soft Motion控制的六自由度工业机器人,设计了一种基于OPC标准协议和Web技术的工业机器人3D(三维)虚拟动态监控系统。基于.NET平台的Windows窗体应用程序框架和My SQL数据库,采用C#语言设计了工业机器人监控客户端程序;通过Co De Sys OPC服务器配置工业机器人各关节角和运动状态为数据项,采用Web GL技术和three.js框架建立了六自由度工业机器人Web3D模型,基于机器人运动学设计了工业机器人3D动态网页。实现了工业机器人关节角和状态数据传输和存储,以及基于Web的工业机器人3D虚拟动态监控功能。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.
文摘针对由Co De Sys Soft Motion控制的六自由度工业机器人,设计了一种基于OPC标准协议和Web技术的工业机器人3D(三维)虚拟动态监控系统。基于.NET平台的Windows窗体应用程序框架和My SQL数据库,采用C#语言设计了工业机器人监控客户端程序;通过Co De Sys OPC服务器配置工业机器人各关节角和运动状态为数据项,采用Web GL技术和three.js框架建立了六自由度工业机器人Web3D模型,基于机器人运动学设计了工业机器人3D动态网页。实现了工业机器人关节角和状态数据传输和存储,以及基于Web的工业机器人3D虚拟动态监控功能。
