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• W2756286502 abstract "With the rapid increase of GPS users, the performance of location-based services (LBS) has gradually become a hot research topic. As the core algorithm of LBS, fast range query processing with massive data become the key problem. Till now, the main structures in this field are R-tree and its varieties. Although they can be adapted to a variety of dynamical data-set, and process insert/deletion in (O(log n)), there are still two essential defects when processing range query in it. Firstly, their time boundary for range query is (O(sqrt{n})). Secondly, their performance are based on heuristic algorithm. Given these two facts, the performance of R-trees is intolerable and unstable. Thus, in this paper, we introduce Geographical Dynamic Cascading Range Tree (GDCRT), a 2D dynamic index tree aiming at geographical range query in points data-set. The main innovation of GDCRT is to make fractional cascaded Range-tree dynamical by applying AVL-tree’s balance principle. For insertion and deletion, its time complexity is (O(log n)), which is equal to R-tree. For range query, its time boundary is (theta (k+log n)), which is lower compared to R-tree series. And final experiment results also prove the correctness and efficiency of our structure." @default.
• W2756286502 created "2017-09-25" @default.
• W2756286502 creator A5025625763 @default.
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• W2756286502 date "2017-01-01" @default.
• W2756286502 modified "2023-09-27" @default.
• W2756286502 title "GDCRT: In-Memory 2D Geographical Dynamic Cascading Range Tree" @default.
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• W2756286502 doi "https://doi.org/10.1007/978-3-319-67952-5_8" @default.
• W2756286502 hasPublicationYear "2017" @default.
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