Abstract
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 GPS 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.
This work is supported by National Natural Science Foundation of China (NSFC) Grant No. 60533040.
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Keywords
- Global Position System
- Global Position System Data
- Global Position System Receiver
- Global Position System Position
- Instant Messenger
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Hu, D.H., Wang, CL. (2007). GPS-Based Location Extraction and Presence Management for Mobile Instant Messenger. In: Kuo, TW., Sha, E., Guo, M., Yang, L.T., Shao, Z. (eds) Embedded and Ubiquitous Computing. EUC 2007. Lecture Notes in Computer Science, vol 4808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77092-3_27
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DOI: https://doi.org/10.1007/978-3-540-77092-3_27
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