Faster and smaller two-level index for network-based trajectories

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Abstract

Two-level indexes have been widely used to handle trajectories of moving objects that are constrained to a network. The top-level of these indexes handles the spatial dimension, whereas the bottom level handles the temporal dimension. The latter turns out to be an instance of the interval-intersection problem, but it has been tackled by non-specialized spatial indexes. In this work, we propose the use of a compact data structure on the bottom level of these indexes. Our experimental evaluation shows that our approach is both faster and smaller than existing solutions.

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Rivera, R., Rodríguez, M. A., & Seco, D. (2018). Faster and smaller two-level index for network-based trajectories. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11147 LNCS, pp. 348–362). Springer Verlag. https://doi.org/10.1007/978-3-030-00479-8_28

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