Adaptive Indexing of Moving Objects with Highly Variable Update Frequencies  

作　　者：陈楠 寿黎但 陈刚 董金祥 

机构地区：[1]College of Computer Science,Zhejiang University

出　　处：《Journal of Computer Science & Technology》2008年第6期998-1014,共17页计算机科学技术学报（英文版）

基　　金：supported in part by Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT0652);the National Natural Science Foundation of China (Grant No. 60603044).

摘　　要：In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures （indexes） have been proposed to handle queries of moving points, for example, the well-known B^x-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named B^y-tree and αB^y-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αB^y-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the B^y-tree and αB^y-tree outperform the B^x-tree in various conditions.In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures （indexes） have been proposed to handle queries of moving points, for example, the well-known B^x-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named B^y-tree and αB^y-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αB^y-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the B^y-tree and αB^y-tree outperform the B^x-tree in various conditions.

关 键 词：spatio-temporal database moving object INDEX 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]