GPS-Based location extraction and presence management for mobile instant messenger

Location is the most essential presence information for mobile users. In this paper, we present an improved time-based clustering technique for extracting significant locations from CPS data stream. This new location extraction mechanism is incorporated with Google Maps for realizing cooperative place annotation on mobile instant messengers (MIM). To enhance the context-awareness of the MIM system, we further develop an ontology-based presence model for inferring the location clues of IM buddies. The GPS-based location extraction algorithm has been implemented on a Smartphone and evaluated using a real-life GPS trace. We show that the proposed clustering algorithm can achieve more accurate results as it considers the time interval of intermittent location revisits. The incorporation of location information with the high-level contexts, such as mobile user's current activity and their social relationship, can achieve more responsive and accurate presence update.