Develop, implement, and improve a web session detection model

More research in web and Information Retrieval is turning towards session-based retrieval rather than single item or query investigation. However, most of the session detection attempts only used simplistic rules (e.g. \"30 mins inactivity creates a new session\"). Up to this point, there are various fuzzy definitions of session, but no general consensus about it in the literature [3]. Whilst comparably little work has involved the mental model about the \"web session\" from real users. In response to these, my research focuses on web session detection involving real users with a comprehensive set of factors identified by them rather than the \"simple fixed timeout\". My objective is to develop a session detection model with corresponding rules for each factor, and then embedded them into a Chrome Extension to automatically detect more accurate web sessions from log data.