Log Data Analysis to Discover User Navigational Behavior: The Case of Adama Science and Technology University Web Users

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Addis Ababa University


The Web has become an exceptional world-wide repository of knowledge. It contains valuable information for all types of knowledge workers; yet, the Web is dynamic and noisy. As of the popularity of WWW by web users, and due to the alarming rate at which the WWW is growing in both the sheer volume of traffic and the complexity of different websites, this growth of the World Wide Web has led to the development of different client side and server side tools that mine the information resources to extract knowledge. Analyzing this data will help the organizations to realize the lifetime value of their clients, and provide them with a more sophisticated structure of the web site and services. A massive amount of data is gathered by Web servers in the form of Web access logs. This is a rich source of information for understanding Web user surfing behavior. As a result of this, exploring user navigation behavior is expected to redesign the web accessing policy based on user requirement and experience. Based on the above expression, to realize web users’ navigational behaviors of Adama Science and Technology University web server log data is used to conduct the current study to describe web user navigational behaviors by applying web usage mining. Web Usage Mining is the process of applying statistical analysis and data mining techniques to discover interesting usage navigation patterns of web users. To explore usage patterns of the Adama Science and Technology University web users the researcher adopted hybrid knowledge discovery approach. Such approach consists of steps, such as problem understanding, data understanding, data preparation, mining user behaviors, evaluation and use of the discovered knowledge. The web log data prepared by using log file viewer tool, to clean irrelevant record from the log data, to categorization, and formatting, using datapreparator-1.7 tool preprocessed log record to converted into the form appropriate for pattern discovery tool by using MS- excel statements. After preprocessing of log file experiments conducted using statistical analysis with datapreparator-1.7 tool and weka 3.7.4 for generating association rule using Apriori and FP Growth algorithm. The result of statistical analysis and data mining techniques shows that social media and entertainment sites are the most frequently accessed once by the web users’ of Adama Science and Technology University. The major challenges that involved in this study are preprocessing of log file due to its large, noisy, and complex nature of log record, and identifying rules and patterns that are potentially interesting. Finally recommendations were done for decision makers ASTU ICT workers, and further researchers to improve the website.



Discover Web User Navigational