The rapid development of Internet has resulted in more and more multimedia in Web content. However, due to the limitation in the bandwidth and huge size of the multimedia data, users always suffer from long time waiting. On the other hand, if we can predict the web object or page that the user most likely will view next while the user is viewing the current page, and pre-fetch the content, then the perceived network latency can be significantly reduced. In this paper, we present an n-gram based model to utilize path profiles of users from very large web log to predict the users' future requests. Our model is based on a simple extension of existing point-based models for such predictions, but our results show that by sacrificing the applicability somewhat one can gain a great deal in prediction precision. Also we present an efficient method to compress the prediction model size so that it can be fitted into the main memory. Our result can potentially be applied to a wide range of applications onthe web, including pro-fetching, enhancement of recommendation systems as well as web caching policies. The experiments based on three realistic web logs have proved the effectiveness of the proposed scheme.
Internet的快速发展导致Web内容中的多媒体越来越多。然而,由于多媒体数据的带宽和巨大的限制,用户总是要等待很长时间。另一方面,如果我们可以预测用户在查看当前页面时最有可能在接下来查看的Web对象或页面,并预取内容,则可以显着减少感知到的网络延迟。在本文中,我们提出了一个基于n-gram的模型,以利用来自非常大的Web日志的用户路径配置文件来预测用户的未来请求。我们的模型基于对此类预测的现有基于点的模型的简单扩展,但是我们的结果表明,通过牺牲适用性,可以在预测精度上获得很大的提高。此外,我们还提出了一种有效的方法来压缩预测模型的大小,以便可以将其拟合到主存储器中。我们的结果可以潜在地应用于Web上的各种应用程序,包括预取,推荐系统的增强以及Web缓存策略。在三个真实的网络日志上进行的实验证明了该方案的有效性。 P>
机译:基于深度信念网络的车联网智能多媒体系统中的短期交通流量预测
机译:多媒体移动云计算中集成工作负载调度和预取的性能
机译:非对称多媒体卫星通信系统-使用非对称多媒体网络的高速Internet / Intranet服务-
机译:互联网多媒体预取的预测系统
机译:通过在暂存器存储系统中进行预取来提高代码覆盖性能
机译:车辆跟踪多媒体物联网系统中嘈杂信道上的联合源和信道速率分配
机译:互联网中多媒体预取的预测系统
机译:实验网络多媒体邮件系统