Web Stats Data Mining网络统计数据挖掘

What can online businesses learn from their web logs?怎么在线业务学习他们的网页记录？ According to据 Xueping Li李学平 of the Intelligent Information and Systems Laboratory, at the University of Tennessee, Knoxville, and colleagues, analyzing the information you can gather about the traffic to your website, whether that’s from raw access data on your server or standalone script-based systems such as Google Analytics, GetClicky, or Statcounter.智能信息系统实验室，田纳西大学的Knoxville和同事们，分析信息，您可以收集有关的交通到您的网站，无论是从原材料获取数据，您的服务器或独立的脚本为基础的系统，例如谷歌分析， GetClicky ，或Statcounter 。

As the Web has grown, so competition has intensified.随着网络的发展，使竞争加剧。 This is most certainly true among ecommerce sites with the likes of Amazon and Barnes & Noble vying for each other’s business just as intensely as news organizations battle for regular readers.这是最确实的电子商务网站，如亚马逊和巴诺争夺对方的业务一样激烈的新闻争夺战组织经常性的读者。 It is even true in the blogosphere where feedcount and post:comment ratios are apparently all important, despite the thin veneer of sociability among the A-listers.即使这是真正的博客在feedcount后：评论比率显然是所有重要的是，尽管薄单板之间的社交性的A - listers 。 Everyone wants more visitors and more interaction because that translates into sales one way or another, whether you are selling books, news, or your personal opinions.人人都希望更多的游客和更多的互动，因为这转化为销售一种或另一种方式，无论你是畅销书 ，新闻，或您的个人意见。

This competition among businesses and the creation of an effective web presence is critical to attracting new customers/readers/community members and retaining current customers/readers/community members and so to the success of the business/community site/blog.这种企业之间的竞争和建立一个有效的网络存在，至关重要的是吸引新客户/读者/社区成员和留住现有的客户/读者/社区成员和如此成功的企业/社区网站/博客。

The features of a website such as its design and security and how it evolves over time influence whether a customer will revisit the site or make a transaction. 该功能的网站，如它的设计和安全性以及它如何演变的影响随着时间的推移客户是否将重新访问该网站，或作交易。

Li and colleagues have developed a way of looking at web logs so that webmasters can evolve their web sites to boost and retain visitors significantly.李和他的同事们开发出一种方式来看待网络日志，以便管理员可以改进他们的网站，以推动和留住游客显着。 Their approach is based on what they describe as simple, yet effective descriptive statistical techniques that reveal the relationship between traffic workload and visitor domains names and geographic locations.他们的做法是根据他们所描述那样简单，但有效的描述性统计技术，揭示了交通之间的关系的工作量和游客域的名称和地理位置。 The regularities and patterns that emerge can shed light on how to design a better web site and enhance its performance.的规律和模式，形成可以揭示如何设计更好的网站，并提高其性能。

They point out that websites that remain unchanged for months or even years after their initial inauguration, cobwebs, you might call them, do with a few notable exceptions, lose their initial burst of customers very quickly.他们指出，网站，保持不变几个月甚至几年后，他们最初的就职典礼，蜘蛛网，你可以给他们打电话，做了几个明显的例外，失去其初始爆裂的客户非常快。 With the rapid growth of the web and intensified competition among businesses, creating and maintaining an effective web presence is a significant challenge.随着迅速增长的网络和加强企业之间的竞争，创造和保持一个有效的网络存在，是一个重大的挑战。 It is no coincidence that perhaps with some exceptions among the “essential” sites, successful websites tend to be those that are dynamic and whose information is the most sophisticated, diverse, and exciting.这并非巧合，也许有一些例外的“基本”的网站，成功的网站往往是那些充满活力，其信息是最复杂的，多种多样的，令人兴奋。

But, there is no point in simply changing for the sake of it, if those changes are not informed by insight gleaned from current and past traffic and visitor interactions.但是，没有任何一点变化只是为了它，如果这些变化并不了解了解见诸于现在和过去的交通和游客的互动。 Of course, there is nothing new in suggesting that we check our stats and adapt our content and design to optimize for visitors.当然，没有什么新的建议，我们要检查我们的统计和调整我们的内容和设计优化供游人使用。 However, the system described by Li and colleagues in their IJECRM publication (full reference below) explains how universal trends might be plucked from those stats.然而，系统描述了李和他的同事在其IJECRM出版物（充分参考下文）解释了如何普遍趋势可能是这艘船的目的地从这些统计资料。 Their proof of principle is based on sourced historical data, but they explain that there is no reason why a site should not do data mining on its live web stats and so become iteratively dynamic.其证明的原则是根据历史数据来源，但他们解释说，没有任何理由网站不应该对数据挖掘的网上实况统计资料，因此成为反复活跃。

Xueping Li, Laigang Song, and Alberto Garcia-Diaz (2008). 李学平，宋莱钢和阿尔贝托加西亚迪亚兹（ 2008年） 。 Adaptive web presence and evolution through web log analysis Int. 自适应网络的存在和演化通过网络日志分析国际。 J. Electronic Customer Relationship Management , 2 (3), 195-214 学者电子客户关系管理， 2 （ 3 ） ， 195-214段