Abstract
With the widespread use of devices with geo-positioning technologies, an unprecedented volume of trajectory data is becoming available. In this paper, we propose and study the problem of multi-range query processing over trajectories, that finds the trajectories that pass through a set of given spatio-temporal ranges. Such queries can facilitate urban planning applications by finding traffic movement flows between different parts of a city at different time intervals. To our best knowledge, this is the first work on answering multi-range queries on trajectories. In particular, we first propose a novel two-level index structure that preserves both the co-location of trajectories, and the co-location of points within trajectories. Next we present an efficient query processing algorithm that employs several pruning techniques at different levels of the index. The results of our extensive experimental studies on two real datasets demonstrate that our approach outperforms the baseline by 1 to 2 orders of magnitude.
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Acknowledgement
This work was partially supported by the National Science Foundation under Grant IIS-13-20791, ARC DP170102726, DP180102050, NSFC 61728204 and 91646204. Zhifeng Bao is a recipient of Google Faculty Award.
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Yadamjav, ME., Choudhury, F.M., Bao, Z., Samet, H. (2018). Efficient Multi-range Query Processing on Trajectories. In: Trujillo, J., et al. Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11157. Springer, Cham. https://doi.org/10.1007/978-3-030-00847-5_20
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