To support a large amount of GPS data generated from various moving objects, the back-end servers usually store low-sampling-rate trajectories. Therefore, no precise position information can be obtained directly from ...To support a large amount of GPS data generated from various moving objects, the back-end servers usually store low-sampling-rate trajectories. Therefore, no precise position information can be obtained directly from the back-end servers and uncertainty is an inherent characteristic of the spatio-temporal data. How to deal with the uncertainty thus becomes a basic and challenging problem. A lot of researches have been rigidly conducted on the uncertainty of a moving object itself and isolated from the context where it is derived. However, we discover that the uncertainty of moving objects can be efficiently reduced and effectively ranked using the context-aware information. In this paper, we focus on context- aware information and propose an integrated framework, Context-Based Uncertainty Reduction and Ranking （CURR）, to reduce and rank the uncertainty of trajectories. Specifically, given two consecutive samplings, we aim to infer and rank the possible trajectories in accordance with the information extracted from context. Since some context-aware information can be used to reduce the uncertainty while some context-aware information can be used to rank the uncertainty, to leverage them accordingly, CURR naturally consists of two stages： reduction stage and ranking stage which complement each other. We also implement a prototype system to validate the effectiveness of our solution. Extensive experiments are conducted and the evaluation results demonstrate the efficiency and high accuracy of CURR.展开更多
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-...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.展开更多
Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predi...Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks（CTBNs） is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.展开更多
To incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM...To incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM（ 1,1 ） model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object＇s movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects.展开更多
通过在U-tree中添加时间戳和速度矢量等时空因素,提出一种基于U-tree的高效率当前及未来不确定位置信息检索的索引结构TPU-tree,可以支持多维空间中不确定移动对象的索引,并提出了一种改进的基于p-bound的MP_BBRQ(modifiedp-bound based...通过在U-tree中添加时间戳和速度矢量等时空因素,提出一种基于U-tree的高效率当前及未来不确定位置信息检索的索引结构TPU-tree,可以支持多维空间中不确定移动对象的索引,并提出了一种改进的基于p-bound的MP_BBRQ(modifiedp-bound based range query)域查询处理算法,能够引入搜索区域进行预裁剪以减少查询精炼阶段所需代价偏高的积分计算.实验仿真表明,采用MP_BBRQ算法的TPU-tree概率查询性能极大地优于传统的TPR-tree索引,且更新性能与传统索引大致相当,具有良好的实用价值.展开更多
基金This work was supported by the National High Technology Research and Development 863 Program of China under Grant No. 2013AA01A603, the Pilot Project of Chinese Academy of Sciences under Grant No. XDA06010600, and the National Natural Science Foundation of China under Grant No. 61402312.
文摘To support a large amount of GPS data generated from various moving objects, the back-end servers usually store low-sampling-rate trajectories. Therefore, no precise position information can be obtained directly from the back-end servers and uncertainty is an inherent characteristic of the spatio-temporal data. How to deal with the uncertainty thus becomes a basic and challenging problem. A lot of researches have been rigidly conducted on the uncertainty of a moving object itself and isolated from the context where it is derived. However, we discover that the uncertainty of moving objects can be efficiently reduced and effectively ranked using the context-aware information. In this paper, we focus on context- aware information and propose an integrated framework, Context-Based Uncertainty Reduction and Ranking （CURR）, to reduce and rank the uncertainty of trajectories. Specifically, given two consecutive samplings, we aim to infer and rank the possible trajectories in accordance with the information extracted from context. Since some context-aware information can be used to reduce the uncertainty while some context-aware information can be used to rank the uncertainty, to leverage them accordingly, CURR naturally consists of two stages： reduction stage and ranking stage which complement each other. We also implement a prototype system to validate the effectiveness of our solution. Extensive experiments are conducted and the evaluation results demonstrate the efficiency and high accuracy of CURR.
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).
文摘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.
基金supported by the National Natural Science Foundation of China (Nos.61100045,61165013,61003142,60902023,and 61171096)the China Postdoctoral Science Foundation (Nos.20090461346,201104697)+3 种基金the Youth Foundation for Humanities and Social Sciences of Ministry of Education of China (No.10YJCZH117)the Fundamental Research Funds for the Central Universities (Nos.SWJTU09CX035,SWJTU11ZT08)Zhejiang Provincial Natural Science Foundation of China (Nos.Y1100589,Y1080123)the Natural Science Foundation of Ningbo,China (No.2011A610175)
文摘Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks（CTBNs） is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.
文摘To incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM（ 1,1 ） model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object＇s movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects.
文摘通过在U-tree中添加时间戳和速度矢量等时空因素,提出一种基于U-tree的高效率当前及未来不确定位置信息检索的索引结构TPU-tree,可以支持多维空间中不确定移动对象的索引,并提出了一种改进的基于p-bound的MP_BBRQ(modifiedp-bound based range query)域查询处理算法,能够引入搜索区域进行预裁剪以减少查询精炼阶段所需代价偏高的积分计算.实验仿真表明,采用MP_BBRQ算法的TPU-tree概率查询性能极大地优于传统的TPR-tree索引,且更新性能与传统索引大致相当,具有良好的实用价值.
