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By default, our servers define as a page any file with one of the following extensions: are considered pages, with the definition of a page varying by server. htm) and files that generate HTML documents (for example. Pages (Also called Page Views): The number of pages viewed during the reporting period.Though not exact, this figure is a relatively accurate representation of the amount of outgoing traffic the server had. KBytes: The amount of data in kilobytes (KB) sent out by the server during the reporting period.The request can be for files, such as an HTML page, graphic image, audio file or CGI script, or queries made by search engine spiders. Hits: The total number of requests that were made to the site during the reporting period.Īny request made to the Web server is logged as a hit.The relationship between hits and files can be thought of as incoming requests and outgoing responses. png), Adobe Acrobat files (.pdf), Macromedia Flash files (.swf), Microsoft Word files (.doc) ASP files (.asp), etc. Files (the outgoing response to a request) include all viable Web file formats, such as HTML files (.html), graphics files (.gif. Websites contain a collection of computer files sent by a remote computer (The web server) to the client (Web browser) as the client requests them.Files: The number of files that have been requested (downloaded) from your site during the reporting period.

, 2004.Visitor Statistics tool tracks the following information about visitor activity (usage) on your site: files, hits, kbytes, pages, referrers, response codes, unique search strings, sites, unique URLs, unique user agents, usernames, and visits. Social Network Analysis: Methods and Applications. Web usage mining: Discovery and applications of usage patterns from web data. Data mining for measuring and improving the success of web sites.

In Proceedings of the Tenth ACM SIGKDD International Con-ference on Knowledge Discovery and Data Mining, pages 671-677, 2004. Identifying early buyers from purchase data. Towards adaptive Web sites: conceptual frame-work and case study. Internet shopping is big business, but more can be done by e-retailers. Capturing evolving visit behavior in clickstream data. Technical Report TR-96050, Department of Computer Science, University of Minnesota, M inneapolis, 1996. Web mining: Pattern discovery from world wide web transactions. Automatic personalization based on web usage mining. IEEE Internet Computing, Industry Report, Jan-uary/February 2003.ī. Recommendations: Item-to- Item Collaborative Filtering. Lessons and challenges from mining retail e-commerce data. Turning browsers into buyers with value-based routing: Methodology enhanced e-commerce, white paper. Web Techniques, January 2000.īeagle Research Group. In Proceedings of the International Conference on Intelligent User Interfaces, pages 106-112, 2000.ĭ. Mining navigation history for recom- mendation. ACM Transactions on Internet Technologies, 3(1):1-27, 2003.

Stephen gomory and robert hoch and juhnyoung lee and mark podlaseck and edith schonberg, July 1999. Harvard Business Review, November-December 2000.Į-Commerce Intelligence: Measuring, Analyzing, and Reporting on Merchan- dising Effectiveness of Online Stores. Prentice Hall, 1997.Ĭenter for Democracy and Technology. Discovering Data Mining: From Concept to Implementation.

One-to-One Web Marketing: Build a Rela-tionship Marketing Strategy One Customer at a Time, 2nd Edition.