Year
2016
Season
Spring
Paper Type
Master's Thesis
College
College of Computing, Engineering & Construction
Degree Name
Master of Science in Computer and Information Sciences (MS)
Department
Computing
NACO controlled Corporate Body
University of North Florida. School of Computing
First Advisor
Dr. Karthikeyan Umapathy
Second Advisor
Dr. Ching-Hua Chuan
Third Advisor
Dr. Sherif Elfayoumy
Department Chair
Dr. Sherif Elfayoumy
College Dean
Dr. Mark A. Tumeo
Abstract
Web designers are expected to perform the difficult task of adapting a site’s design to fit changing usage trends. Web analytics tools give designers a window into website usage patterns, but they must be analyzed and applied to a website's user interface design manually. A framework for marrying live analytics data with user interface design could allow for interfaces that adapt dynamically to usage patterns, with little or no action from the designers. The goal of this research is to create a framework that utilizes web analytics data to automatically update and enhance web user interfaces. In this research, we present a solution for extracting analytics data via web services from Google Analytics and transforming them into reporting data that will inform user interface improvements. Once data are extracted and summarized, we expose the summarized reports via our own web services in a form that can be used by our client side User Interface (UI) framework. This client side framework will dynamically update the content and navigation on the page to reflect the data mined from the web usage reports. The resulting system will react to changing usage patterns of a website and update the user interface accordingly. We evaluated our framework by assigning navigation tasks to users on the UNF website and measuring the time it took them to complete those tasks, one group with our framework enabled, and one group using the original website. We found that the group that used the modified version of the site with our framework enabled was able to navigate the site more quickly and effectively.
Suggested Citation
Carle, William R. II, "Active Analytics: Adapting Web Pages Automatically Based on Analytics Data" (2016). UNF Graduate Theses and Dissertations. 629.
https://digitalcommons.unf.edu/etd/629
Included in
Computer and Systems Architecture Commons, Data Storage Systems Commons, Digital Communications and Networking Commons