Introduction:
Besides mining Web contents and Web linkage structures, another important task for Web mining is Web usage mining, which mines Weblog records to discover user access patterns of Web pages. Analyzing and exploring regularities in We blog records can identify potential customers for electronic commerce, enhance the quality and delivery of Internet information services to the end user, and improve Web server system performance.
A Web server usually registers a (Web) log entry, or Weblog entry, for every access of a Web page. It includes the URL requested, the IP address from which the request originated, and a timestamp. For Web-based e-commerce servers, a huge number of Web access log records are being collected. Popular websites may register Weblog records in the order of hundreds of megabytes every day. Weblog databases provide rich information about Web dynamics. Thus it is important to develop sophisticated Weblog mining techniques.
In developing techniques for Web usage mining, we may consider the following. First, although it is encouraging and exciting to imagine the various potential applications of Weblog file analysis, it is important to know that the success of such applications depends on what and how much valid and reliable knowledge can be discovered from the large raw log data. Often, raw Weblog data need to be cleaned, condensed, and transformed in order to retrieve and analyze significant and useful information. In principle, these preprocessing methods are similar to those discussed in Chapter 2, although Weblog customized preprocessing is often needed.
Second, with the available URL, time, IP address, and Web page content information, a multidimensional view can be constructed on the Weblog database, and multidimensional OLAP analysis can be performed to find the top N users, top N accessed Web pages, most frequently accessed time periods, and so on, which will help discover potential customers, users, markets, and others. Third, data mining can be performed on Weblog records to find association patterns, sequential patterns, and trends of Web accessing. For Web access pattern mining, it is often necessary to take further measures to obtain additional information of user traversal to facilitate detailed Weblog analysis. Such additional information may include user browsing sequences of the Web pages in the Web server buffer.
With the use of such Weblog files, studies have been conducted on analyzing system performance, improving system design by Web caching, Web page perfecting, and Web page swapping; understanding the nature of Web traffic; and understanding user reaction and motivation. For example, some studies have proposed adaptive sites: websites that improve themselves by learning from user access patterns. Weblog analysis may also help build customized Web services for individual users.
Because Weblog data provide information about what kind of users will access what kind of Web pages, Weblog information can be integrated with Web content and Web linkage structure mining to help Web page ranking, Web document classification, and the construction of a multilayered Web information base as well. A particularly interesting application of Web usage mining is to mine a user’s interaction history and search context on the client side to extract useful information for improving the ranking accuracy for the given user. For example, if a user submits a keyword query “Java” to a search engine, and then selects “Java programming language” from the returned entries for viewing, the system can infer that the displayed snippet for this Web page is interesting to the user. It can then raise the rank of pages similar to “Java programming language” and avoid presenting distracting pages about “Java Island.” Hence the quality of search is improved, because search is contextualized and personalized.
