Compression of uncertain trajectories in road networks

Massive volumes of uncertain trajectory data are being generated by GPS devices. Due to the limitations of GPS data, these trajectories are generally uncertain. This state of affairs renders it is attractive to be able to compress uncertain trajectories and to be able to query the trajectories efficiently without the need for (full) decompression. Unlike existing studies that target accurate trajectories, we propose a framework that accommodates uncertain trajectories in road networks. To address the large cardinality of instances of a single uncertain trajectory, we exploit the similarity between uncertain trajectory instances and provide a referential representation. First, we propose a reference selection algorithm based on the notion of Fine-grained Jaccard Distance to efficiently select trajectory instances as references. Then we provide referential representations of the different types of information contained in trajectories to achieve high compression ratios. In particular, a new compression scheme for temporal information is presented to take into account variations in sample intervals. Finally, we propose an index and develop filtering techniques to support efficient queries over compressed uncertain trajectories. Extensive experiments with real-life datasets offer insight into the properties of the framework and suggest that it is capable of outperforming the existing state-of-the-art method in terms of both compression ratio and efficiency.