Abstract:
Now-a-days the use of technology is rapidly growing in the workplaces and a large number of people are using desktop computers for their personal and professional tasks. As a result of this growth, the large amount of data is been gathered and it’s become difficult to retrieve and manage this huge amount of data in a beneficial way. People usually face difficulty in managing documents and finding relevant files when they want to do specific task in hand. We believe that pervasive monitoring of user’s desktop activities and then mining of user’s periodic behavior can be useful in assisting the future tasks of user. A number of studies have been carried out on monitoring user desktop routine activity. However, these studies have not utilized user’s temporal routine activity patterns for assistance. It was a need to develop a desktop assistant by mining user routine behavior in accordance with the time and date more specifically weekdays and weekends. This work detects and classifies meaningful activities from large data and then models these activities into user’s one day and routine behaviors. This system reads log file and detect some meaningful activities from the large data, after identifying activities the system model these activities into user’s one day activity, then system model routine activities based on one day activity models and evaluates probability measures of user’s routine activities. After getting best results the system finds the best models of routine behavior which may use for the prediction of next possible routine activity of the user based on logged data set. It mines the best models of routine activities which may be useful for the prediction of next possible activity of the user. After a comparative results analysis of standard machine learning and data mining algorithms this research found Decision Tree best for classification of user’s activities with highest mean score of 0.92. Moreover this research has found Decision Tree as best algorithm for prediction of user’s future possible routine activities with 0.84 as highest score. The system has predicted future routine activities of a single user on Tuesday with an accuracy of 92%. This research is basically the implementation of a novel approach for mining user’s periodic desktop routine activity in accordance with the time, date and day.
