Optimal Index Partitioning of Main-Memory Based TPR*-Tree for Real-Time Tactical Moving Objects
Optimal Index Partitioning of Main-Memory Based TPR*-Tree for Real-Time Tactical Moving Objects
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DOI：
10.1109/bigcomp.2018.00070
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Jiwan Lee;B. Hong;Jaegi Hong;Chum-Su Kim;Woochan Kim
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文献类型：
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作者：
Jiwan Lee;B. Hong;Jaegi Hong;Chum-Su Kim;Woochan Kim
用于实时战术移动对象的基于主存的TPR*树对于实时预测查询处理是最有效的。雷达采集到的目标物体除了海中的低速物体外，还混有航空的高速物体，基于主存的TPR*树存在MBR重叠面积增大的问题。本文提出了一种最佳索引分区，以最小化基于主存的 TPR* 树中 MBR 的重叠。提出了一种优化分区算法，该算法既最小化时空预测查询处理的成本，又最小化划分索引的组合处理的成本。
The main-memory based TPR*-tree for real-time tactical moving objects is most effective for real-time prediction query processing. The target objects collected from radar are mixed with high-speed objects of aviation in addition to the low-speed objects in the sea, and there is a problem that the overlap area of the MBR increases in the main-memory based TPR*-tree. This paper proposes an optimal index partitioning to minimize the overlap of MBR in main-memory based TPR*-tree. An optimization partitioning algorithm is proposed in which both the cost of space-time prediction query processing is minimized and the cost of combining processing of divided indexes is minimized.