Abstract
Position uncertainty is one key feature of moving objects. Existing uncertain moving objects indexing technology aims to improve the efficiency of querying. However, when moving objects’ positions update frequently, the existing methods encounter a high update cost. We purpose an index structure for frequent position updates: HGTPU-tree, which decreases cost caused by frequent position updates of moving objects. HGTPU-tree reduces the number of disk I/Os and update costs by using bottom-up update strategy and reducing same group moving objects updates. Furthermore we purpose moving object group partition algorithm STSG (Spatial Trajectory of Similarity Group) and uncertain moving object similar group update algorithm. Experiments show that HGTPU-tree reduces memory cost and increases system stability compared to existing bottom-up indexes. We compared HGTPU-tree with TPU-tree, GTPU-tree and TPU2M-tree. Results prove that HGTPU-tree is superior to other three state-of-the-art index structures in update cost.
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Zhang, M., Li, B., Wang, K. (2019). HGTPU-Tree: An Improved Index Supporting Similarity Query of Uncertain Moving Objects for Frequent Updates. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_10
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