本文選題：Predictive + Query　； 參考：《計算機科學》2017年S1期

【摘要】：隨著智能交通、基于位置的廣告投放、移動對象監(jiān)測等應用的廣泛發(fā)展,如何快速預測未來某一時間點的對象的位置成為目前的一個研究熱點。提出了一種新穎的AP-I(Adaptive Predication-Index)索引,其在歷史軌跡數(shù)據(jù)缺乏的情況下,能夠追蹤移動對象的當前位置,大幅提高預測查詢的運行效率。與現(xiàn)有的Predictive Tree~([4])索引相比,AP-Index能有效地挖掘移動對象之間的路徑關聯(lián)性,避免大量的索引更新和重建操作,提高索引效率。同時,通過引入AP(Adaptive Probability)以及Pruning操作,進一步減小AP-I,提高索引的命中率和查詢效率。實驗表明,與Predictive Tree相比,在保證同等查詢效率的基礎上,AP-I實現(xiàn)了更優(yōu)的準確度、更新效率和空間效率。
[Abstract]:With the wide development of intelligent transportation, location-based advertising, mobile object monitoring and other applications, how to quickly predict the location of objects at a certain point in the future has become a research hotspot at present. In this paper, a novel AP-I (Adaptive Predication-Index) index is proposed, which can track the current position of moving objects in the absence of historical track data and greatly improve the efficiency of prediction query. Compared with the existing predictive tree ([4]) index, AP-Index can effectively mine the path correlation between moving objects, avoid a large number of index updating and reconstruction operations, and improve the index efficiency. At the same time, by introducing AP (Adaptive probability) and pruning operations, the AP-I is further reduced, and the hit rate and query efficiency of the index are improved. Experimental results show that AP-I achieves better accuracy update efficiency and spatial efficiency than predictive Tree on the basis of ensuring the same query efficiency.
【作者單位】： 深圳職業(yè)技術學院計算機工程學院;
【分類號】：O157.5

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本文編號：2070359