支持频繁位置更新的不确定移动对象索引策略

移动数据采集和处理技术的迅速发展给研究人员提出了新的应用需求,如何在频繁位置更新应用中索引不确定移动对象的当前及未来位置信息成为当前的研究热点之一.TPU树是针对不确定移动对象的当前及未来位置信息索引的策略,其具有较高的概率域查询效率,但是其采用的传统自顶向下更新算法,存在频繁位置更新效率低下的问题.通过在TPU树上增加一个记录不确定移动对象状态特征的更新备忘录(UM)内存结构,文中提出了一种支持频繁位置更新的不确定移动对象索引策略TPU2M树,并在此基础之上提出了一种改进的基于备忘录(MMBU/I)的更新/插入算法.代价分析和实验仿真表明,采用MMBU/I算法的TPU2M树频繁更新性能大大优于TPU树和ABx树索引,且概率查询性能与传统索引大致相当,因此具有很好的实用价值和广泛的应用前景.     

网络受限移动对象过去、现在及将来位置的索引

提出了一种适合于网络受限移动对象数据库的动态轨迹R树索引结构(network-constrained moving objects dynamic trajectory R-Tree,简称NDTR-Tree).NDTR-Tree不仅能够索引移动对象的整个历史轨迹,而且能够动态地索引和维护移动对象的当前及将来位置.为了比较相关索引结构及算法的性能,进行了详细的实验.实验结果表明,与现有的基于道路网络的移动对象索引方法如MON-Tree和FNR-Tree等相比,NDTR-Tree有效地提高了对网络受限移动对象动态全轨迹的查询处理性能.     

一种基于受限网络的移动对象索引

为了有效地支持城市交通网络中移动对象的过去、现在和将来的轨迹查询,在基于模拟预测的位置表示模型基础上,提出了一种两层R树加上一个表结构的复合索引结构AUC(Adaptive Unit Compounding).根据城市交通网的特征,采用了一种带有环形交叉口的元胞自动机模型模拟移动对象的将来轨迹,并用线性回归和圆弧曲线拟合分别得到对象在规则路段和交叉口的轨迹预测方程;根据移动对象的运动特性,采用了一种新的自适应单元(AU)作为索引结构的基本单位.实验表明,AUC索引的查询和更新性能都要优于TPR树和TB树.     

基于速度分布的移动对象混合索引方法

TPR*树是目前广泛使用的移动对象当前及未来位置预测索引技术,但是其频繁更新及查询性能随着时间变化而急遽下降.文中提出了一种基于速度分布的移动对象混合索引HVTPR树,综合考虑移动对象在速度域和空间域中的分布,首先在速度域中对移动对象集进行规则划分,根据速度矢量大小将移动对象映射到不同的速度桶,每个速度桶中移动对象具有相近的速度矢量;对每个速度桶中的移动对象,则利用TPR树进行索引.HVTPR树索引增加了一个建于移动对象标识上的Hash辅助索引结构,并采用增强的自底向上更新(EBUU)算法以提高其频繁更新性能,具有很好的动态更新性能和并发性.实验表明,采用EBUU算法的HVTPR树索引动态更新及查询性能优于TPR*树等通用索引技术.     

一种基于R树和四叉树的移动对象空间数据库混合索引结构

由于实时跟踪移动的空间对象需要不断的更新空间数据库的索引从而导致系统执行效率非常低下，传统静态的空间索引结构性能并不是很好的工作在动态环境下。提出了一种称为Q＋R树的移动空间对象混合索引结构，阐述了该数据结构的生成、更新、查询方面算法思想，最后给出了Q＋R树的试验分析结论。     

支持固定网络中频繁更新的移动对象混合索引模型

IMOFN是一种支持固定网络中频繁更新的移动对象混合索引模型,它由一棵描述固定道路网络的2D R*-Tree、一组对时间进行索引的1D R*-Tree和一个存储移动对象实时数据的Hash数组组成.IMOFN既管理了移动对象实时位置,支持位置的频繁更新;又保存了历史轨迹,提供了范围查询、拓扑查询以及轨迹查询等多种有效的实时和历史查询功能.通过实验与FNR-Tree和MON-Tree进行性能比较,证实了IMOFN模型高效的查询能力.     

道路网络中移动对象的索引研究

为了能有效地实现网络中移动对象的过去、当前和将来轨迹的查询,提出了一种L2R索引,它由两层R树和一个链表结构组成。两层R树用以索引道路网络和移动对象过去的运动,对象当前的位置和将来的预测轨迹信息保存在链表中。L2R索引不仅可以支持网络中的移动对象的轨迹查询,尤其是可方便的在纵向链表中查询在同条路线上的所有对象。在此索引基础上文中实施了对移动对象的范围查询和点查询,最后通过实验表明L2R结构的索引和查询性能均要优越于TPR树。     

支持频繁更新的移动对象混合索引方法

TPR-tree是目前广泛使用的移动对象当前及未来位置索引技术,但是其频繁更新性能低下.通过在TPR-tree上增加一个指向索引树中间节点的直接访问表（direct-access table）内存结构和建于叶节点之上的Hash辅助索引结构,提出了一种支持频繁更新的移动对象混合索引HTPR-tree,并提出了基于HTPR-tree的扩展自底向上（EBUU）更新算法.性能分析和实验表明,采用EBUU算法的HTPRtree动态更新性能大大高于TPR^＊-tree等索引,而查询性能仅仅稍逊.     

一种基于城市交通网络的移动对象全时态索引

高效地管理移动对象以支持查询是一个重要课题.为了支持在城市交通网络上的移动对象过去、现在和将来位置查询,提出了一种新的索引技术.首先提出基于模拟预测的位置表示模型来改进对移动对象将来运动轨迹的预测精度;其次根据城市交通网的特征,设计了一种全新的动态结构自适应单元(AU),将其开发为一个基于R树的索引结构(current-Au);最后在AU的基础上进行扩展(past-AU)使其支持移动对象历史轨迹查询并且避免了大量的死空间.实验证明,AU索引优于传统的TPR树和TB树索引.     

面向频繁位置更新的不确定移动对象索引策略

位置不确定性是移动对象的重要特点之一。已有的不确定移动对象索引技术旨在提高查询效率,但是当移动对象位置频繁更新时,存在更新代价较大的问题。针对移动对象频繁位置更新引起的开销增加问题,在TPU-tree索引结构上支持移动对象群组划分策略,给出了一种适用于频繁位置更新的索引结构GTPUtree。在此基础上提出了基于空间轨迹相似度的群组划分算法STSG(spatial trajectory of similarity group)和不确定移动对象群组更新算法。GTPU-tree通过减少同一分组中移动对象的更新次数,降低磁盘I/O次数,从而降低更新代价。通过实验对基于GTPU-tree和TPU2M-tree等索引结构的算法效率进行了对比分析,结果表明GTPU-tree相比于TPU2M-tree在移动对象数量较大时,GTPU-tree的更新代价将低于TPU2M-tree;与TPUtree相比插入性能提高约30%,更新代价降低约35%。     

Online Approach for Spatio-Temporal Trajectory Data Reduction for Portable Devices

As location data are widely available to portable devices, trajectory tracking of moving objects has become an essential technology for most location-based services. To maintain such streaming data of location updates from mobile clients, conventional approaches such as time-based regular location updating and distance-based location updating have been used. However, these methods suffer from the large amount of data, redundant location updates, and large trajectory estimation errors due to the varying speed of moving objects. In this paper, we propose a simple but effcient online trajectory data reduction method for portable devices. To solve the problems of redundancy and large estimation errors, the proposed algorithm computes trajectory errors and finds a recent location update that should be sent to the server to satisfy the user requirements. We evaluate the proposed algorithm with real GPS trajectory data consisting of 17201 trajectories. The intensive simulation results prove that the proposed algorithm always meets the given user requirements and exhibits a data reduction ratio of greater than 87% when the acceptable trajectory error is greater than or equal to 10 meters.     

Indexing in-network trajectory flows

Indexing moving objects (MO) is a hot topic in the field of moving objects databases since many years. An impressive number of access methods have been proposed to optimize the processing of MO-related queries. Several methods have focused on spatio-temporal range queries, which represent the foundation of MO trajectory queries. Surprisingly, only a few of them consider that the objects movements are constrained. This is an important aspect for several reasons ranging from better capturing the relationship between the trajectory and the network space to more accurate trajectory representation with lower storage requirements. In this paper, we propose T-PARINET, an access method to efficiently retrieve the trajectories of objects moving in networks. T-PARINET is designed for continuous indexing of trajectory data flows. The cornerstone of T-PARINET is PARINET, an efficient index for historical trajectory data. The structure of PARINET is based on a combination of graph partitioning and a set of composite B⁺-tree local indexes. Because the network can be modeled using graphs, the partitioning of the trajectory data makes use of graph partitioning theory and can be tuned for a given query load and a given data distribution in the network space. The tuning process is built on a good quality cost model that is supplied with PARINET. The advantage of having a cost model is twofold; it allows a better integration of the index into the query optimizer of any DBMS, and it permits tuning the index structure for better performance. The tuning process can be performed before the index creation in the case of historical data or online in the case of indexing data flows. In fact, massive online updates can degrade the index quality, which can be measured by the cost model. We propose a specific maintenance process that results into T-PARINET. We study different types of queries and provide an optimized configuration for several scenarios. T-PARINET can easily be integrated into any RDBMS, which is an essential asset particularly for industrial or commercial applications. The experimental evaluation under an off-the-shelf DBMS shows that our method is robust. It also significantly outperforms the reference R-tree-based access methods for in-network trajectory databases.     

基于动态网格的移动对象索引

在移动对象数据库中,移动对象的数量可能会经常变化,这就给索引技术提出了新的挑战。移动对象索引技术的效率是移动对象数据库的一个重要研究课题。为了防止数据库由于移动对象数量的变化而导致性能锐减,本文在网格文件索引的基础上提出了一种动态网格索引技术。通过实验比较显示,它相对于静态索引具有更好的适应性。     

Designing a location update strategy for free-moving and network-constrained objects with varying velocity

Spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been made to address the issue of when and how to update location information of moving objects. Previous work shifts the workload of processing updates to each object which usually has limited CPU and battery capacities. This results in a tremendous processing overhead for each moving object. In this paper, we focus on designing efficient update strategies for two important types of moving objects, free-moving objects （FMOs） and network-constrained objects （NCOs）, which are classified based on object movement models. For FMOs, we develop a novel update strategy, namely the FMO update strategy （FMOUS）, to explicitly indicate a time point at which the object needs to update location information. As each object knows in advance when to update （meaning that it does not have to continuously check）, the processing overhead can be greatly reduced. In addition, the FMO update procedure （FMOUP） is designed to efficiently process the updates issued from moving objects. Similarly, for NCOs, we propose the NCO update strategy （NCOUS） and the NCO update procedure （NCOUP） to inform each object when and how to update location information. Exten- sive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed update strategies.     

Indexing Future Trajectories of Moving Objects in a Constrained Network

Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.     

基于大数据分析的移动对象轨迹预测方法

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。     

Efficient Location Updates for Continuous Queries over Moving Objects

The significant overhead related to frequent location updates from moving objects often results in poor performance. As most of the location updates do not affect the query results, the network bandwidth and the battery life of moving objects are wasted. Existing solutions propose lazy updates, but such techniques generally avoid only a small fraction of all unnecessary location updates because of their basic approach (e.g., safe regions, time or distance thresholds). Furthermore, most prior work focuses on a simplified scenario where queries are either static or rarely change their positions. In this study, two novel efficient location update strategies are proposed in a trajectory movement model and an arbitrary movement model, respectively. The first strategy for a trajectory movement environment is the Adaptive Safe Region (ASR) technique that retrieves an adjustable safe region which is continuously reconciled with the surrounding dynamic queries. The communication overhead is reduced in a highly dynamic environment where both queries and data objects change their positions frequently. In addition, we design a framework that supports multiple query types (e.g., range and c-kNN queries). In this framework, our query re-evaluation algorithms take advantage of ASRs and issue location probes only to the affected data objects, without flooding the system with many unnecessary location update requests. The second proposed strategy for an arbitrary movement environment is the Partition-based Lazy Update (PLU, for short) algorithm that elevates this idea further by adopting Location Information Tables (LITs) which (a) allow each moving object to estimate possible query movements and issue a location update only when it may affect any query results and (b) enable smart server probing that results in fewer messages. We first define the data structure of an LIT which is essentially packed with a set of surrounding query locations across the terrain and discuss the mobile-side and server-side processes in correspondence to the utilization of LITs. Simulation results confirm that both the ASR and PLU concepts improve scalability and efficiency over existing methods.     

基于前缀投影技术的大规模轨迹预测模型

智能手机、车载GPS终端、可穿戴设备产生了海量的轨迹数据,这些数据不仅描述了移动对象的历史轨迹,而且精确地反映出移动对象的运动特点.已有轨迹预测方法的不足在于：不能同时兼具预测的准确性和时效性,有效的轨迹预测受限于路网等局部空间范围,无法处理复杂、大规模位置数据.为了解决上述问题,针对海量移动对象轨迹数据,结合频繁序列模式发现的思想,提出了基于前缀投影技术的轨迹预测模型PPTP（prefix projection based trajectory prediction model）,包含两个关键步骤：（1）挖掘频繁轨迹模式,构造投影数据库并递归挖掘频繁前序轨迹模式;（2）轨迹匹配,以不同频繁序列模式作为前缀增量式扩展生成频繁后序轨迹,将大于最小支持度阈值的最长连续轨迹作为结果输出.算法的优势在于：可以通过较短的频繁序列模式,增量式生成长轨迹模式;不会产生无用的候选轨迹,弥补频繁模式挖掘计算代价较高的不足.利用真实大规模轨迹数据进行多角度实验,表明PPTP轨迹预测算法具有较高的预测准确性,相对于1阶马尔可夫链预测算法,其平均预测准确率可以提升39.8%.基于所提出的轨迹预测模型,开发了一个通用的轨迹预测系统,能够可视化输出完整的轨迹路线,为用户路径规划提供辅助决策支持.     

A connectivity index for moving objects in an indoor cellular space

With the currently available indoor positioning devices such as RFID, Bluetooth and WI-FI, the locations of moving objects constitute an important foundation for a variety of applications such as the tracking of moving objects, security and way finding. Many studies have proven that most individuals spend their lives in indoor environments. Therefore, in this paper, we propose a new index structure for moving objects in cellular space. The index is based on the connectivity (adjacency) between the indoor environment cells and can effectively respond to the spatial indoor queries and enable efficient updates of the location of a moving object in indoor space. An empirical performance study suggests that the proposed indoor-tree in terms of measurements and performance is effective, efficient and robust.