With the rapid development of Internet technologies and the increased number of possibilities entitled to users, the question of analyzing commercial websites is rather acute. It is necessary to evaluate e-commerce web pages since their usability and other features influence the potential consumers’ choices to a great extent. As Andreolini et al. remark, the common coarse grain analytical tool utilized for websites’ performance assessment is no longer sufficient to generate reliable results. In contrast, fine grain analysis should be conducted, which will enable a thorough study of e-commerce sites’ scalability and availability.
The major asset of e-commerce websites is their functioning as a standardized business orientation technique put in use via web resources. Such websites typically involve specialized services and data flow designed for a target group of consumers. Due to this multi-faceted function of a website, its generation and utilization necessitate the deployment of special technologies (Andreolini et al.). These technologies have developed into what is now known as an e-commerce system.
In the past, a coarse grain system was predominantly used to assess any website’s components, including the hardware- and software-related ones. However, as Andreolini et al. justly argue, the use of such an approach has lost its actuality and reliability. Therefore, it is now recommended to apply the fine-grain system, which enables the researchers to single out the software elements that cause a bottleneck in the system (Andreolini et al.). What is more, fine-grain analysis allows identifying the effect of hardware enhancements on the system. The fine grain analysis is successfully used in various fields of research, including social network websites (Barba-González et al. 22). This method of evaluating websites has proven to be successful, which makes it a useful tool for analysis.
Works Cited
Andreolini, Mauro, et al. “Fine Grain Performance Evaluation of E-Commerce Sites”. ACM SIGMETRICS Performance Evaluation Review, vol. 32, no. 3, 2004.
Barba-González, Cristóbal, et al. “A Fine Grain Sentiment Analysis with Semantics in Tweets.” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 3, no. 6, 2016, pp. 22-28.