Abstract
The wide usage of location aware devices, such as GPS-enabled cellphones or PDAs, generates vast volumes of spatiotemporal streams modeling objects movements, raising management challenges, such as efficient storage and querying. Therefore, compression techniques are inevitable also in the field of moving object databases. Moreover, due to erroneous measurements from GPS devices, the problem of matching the location recordings with the underlying traffic network has recently gained the attention of the research community. So far, the proposed compression techniques are not designed for network constrained moving objects, while map matching algorithms do not consider compression issues. In this paper, we propose solutions tackling the combined, map matched trajectory compression problem, the efficiency of which is demonstrated through an experimental evaluation using a real trajectory dataset.
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Kellaris, G., Pelekis, N., Theodoridis, Y. (2009). Trajectory Compression under Network Constraints. In: Mamoulis, N., Seidl, T., Pedersen, T.B., Torp, K., Assent, I. (eds) Advances in Spatial and Temporal Databases. SSTD 2009. Lecture Notes in Computer Science, vol 5644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02982-0_27
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DOI: https://doi.org/10.1007/978-3-642-02982-0_27
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