First Look on Web Mining Techniques to Improve Business Intelligence of E-Commerce Applications

First Look on Web Mining Techniques to Improve Business Intelligence of E-Commerce Applications

G. Sreedhar, A. Anandaraja Chari
DOI: 10.4018/978-1-5225-2031-3.ch018
(Individual Chapters)
No Current Special Offers


Web Data Mining is the application of data mining techniques to extract useful knowledge from web data like contents of web, hyperlinks of documents and web usage logs. There is also a strong requirement of techniques to help in business decision in e-commerce. Web Data Mining can be broadly divided into three categories: Web content mining, Web structure mining and Web usage mining. Web content data are content availed to users to satisfy their required information. Web structure data represents linkage and relationship of web pages to others. Web usage data involves log data collected by web server and application server which is the main source of data. The growth of WWW and technologies has made business functions to be executed fast and easier. As large amount of transactions are performed through e-commerce sites and the huge amount of data is stored, valuable knowledge can be obtained by applying the Web Mining techniques.
Chapter Preview

Model And Analysis

The page load times of some of the popular E-commerce websites are considered for study to assess the performance and speed of the online business Websites. The study considers the analysis on page load time of the home page of E-commerce websites using statistical control chart analysis. The Home page download times of the websites are observed using GTmetrix developed by, a web performance hosting tool (GTmetrix, 2016). The study evolves the performance of 100 Popular E-commerce websites into five different categories:

  • 1.

    Multiple Product websites

  • 2.

    Electronic Product websites

  • 3.

    Service websites

  • 4.

    Fashion and Textile websites

  • 5.

    Specific websites

The website download times are compiled using GTmetrix during March 2016 and are shown in Table 1, Table 2, Table 3, Table 4, and Table 5.

Complete Chapter List

Search this Book: