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.     

Effcient 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...     

非确定性查询及其在无约束运动中的应用

为了追踪和记录空间移动对象的运动轨迹，需要记录其在各个采样时刻的空间坐标数据。但是对于采样时刻之间的对象坐标以及上一采样时刻结束，下一采样时刻到来之前的坐标数据的确定仅仅是一种估计，这种估计本身存在一定的非确定性。该文提出了针对时空数据非确定性的时空最近点查询即NN查询（Nearest Neighbor query）的算法NNU（Nearest Neighbur query with Uncenainty），并介绍了其在二维无约束空间运动中的应用。     

Processing moving queries over moving objects using motion-adaptive indexes

This paper describes a motion-adaptive indexing scheme for efficient evaluation of moving continual queries (MCQs) over moving objects. It uses the concept of motion-sensitive bounding boxes (MSBs) to model moving objects and moving queries. These bounding boxes automatically adapt their sizes to the dynamic motion behaviors of individual objects. Instead of indexing frequently changing object positions, we index less frequently changing object and query MSBs, where updates to the bounding boxes are needed only when objects and queries move across the boundaries of their boxes. This helps decrease the number of updates to the indexes. More importantly, we use predictive query results to optimistically precalculate query results, decreasing the number of searches on the indexes. Motion-sensitive bounding boxes are used to incrementally update the predictive query results. Furthermore, we introduce the concepts of guaranteed safe radius and optimistic safe radius to extend our motion-adaptive indexing scheme to evaluating moving continual k-nearest neighbor (kNN) queries. Our experiments show that the proposed motion-adaptive indexing scheme is efficient for the evaluation of both moving continual range queries and moving continual kNN queries.     

An adaptive updating protocol for reducing moving object database workload

In the last decade, spatio-temporal database research focuses on the design of effective and efficient indexing structures in support of location-based queries such as predictive range queries and nearest neighbor queries. While a variety of indexing techniques have been proposed to accelerate the processing of updates and queries, not much attention has been paid to the updating protocol, which is another important factor affecting the system performance. In this paper, we propose a generic and adaptive updating protocol for moving object databases with less number of updates between objects and the database server, thereby reducing the overall workload of the system. In contrast to the approach adopted by most conventional moving object database systems where the exact locations and velocities last disclosed are used to predict their motions, we propose the concept of Spatio-temporal safe region to approximate possible future locations. Spatio-temporal safe regions provide larger space of tolerance for moving objects, freeing them from location and velocity updates as long as the errors remain predictable in the database. To answer predictive queries accurately, the server is allowed to probe the latest status of objects when their safe regions are inadequate in returning the exact query results. Spatio-temporal safe regions are calculated and optimized by the database server with two contradictory objectives: reducing update workload while guaranteeing query accuracy and efficiency. To achieve this, we propose a cost model that estimates the composition of active and passive updates based on historical motion records and query distribution. More system performance improvements can be obtained by cutting more updates from the clients, when the users of system are comfortable with incomplete but accuracy bounded query results. We have conducted extensive experiments to evaluate our proposal on a variety of popular indexing structures. The results confirm the viability, robustness, accuracy and efficiency of our proposed protocol.     

MOVIES: indexing moving objects by shooting index images

With the exponential growth of moving objects data to the Gigabyte range, it has become critical to develop effective techniques for indexing, updating, and querying these massive data sets. To meet the high update rate as well as low query response time requirements of moving object applications, this paper takes a novel approach in moving object indexing. In our approach, we do not require a sophisticated index structure that needs to be adjusted for each incoming update. Rather, we construct conceptually simple short-lived index images that we only keep for a very short period of time (sub-seconds) in main memory. As a consequence, the resulting technique MOVIES supports at the same time high query rates and high update rates, trading this property for query result staleness. Moreover, MOVIES is the first main memory method supporting time-parameterized predictive queries. To support this feature, we present two algorithms: non-predictive MOVIES and predictive MOVIES. We obtain the surprising result that a predictive indexing approach—considered state-of-the-art in an external-memory scenario—does not scale well in a main memory environment. In fact, our results show that MOVIES outperforms state-of-the-art moving object indexes such as a main-memory adapted B ^x -tree by orders of magnitude w.r.t. update rates and query rates. In our experimental evaluation, we index the complete road network of Germany consisting of 40,000,000 road segments and 38,000,000 nodes. We scale our workload up to 100,000,000 moving objects, 58,000,000 updates per second and 10,000 queries per second, a scenario at a scale unmatched by any previous work.     

A scalable constraint-based Q-hash indexing for moving objects

In this paper, we develop a Q-hash index structure to efficiently store the position of moving objects. An environment of moving objects contains continuously changing locations which are hard to index using traditional index structures such as R-trees, QuadTrees and their variants. In order to answer the queries accurately, one of the problems faced in storing these positions is the number of updates that have to be made to the database whenever locations change. The high maintenance overhead on updates leads to performance degradation of these index structures; additionally, it makes the database very bulky which results in very poor performance in terms of query execution time. One of the main objectives of the structure we propose is to minimize the number of updates to the database to an optimal number so that the accuracy and response time of the query result are not compromised and at the same time the number of wireless communications can be reduced. The indexing is done using a hashing technique where the hashing function makes use of a region based QuadTree structure. To improve the efficiency of the query processing our index structure helps us define constraints over speed, direction and location of the moving object at the device level which controls the number of updates. In addition, in order to answer different query types efficiently at all levels we propose a three-tier (moving object, regional server, central repository) architecture. Our extensive performance evaluation and comparison of the proposed technique concludes that our scheme outperforms existing Q + R-tree and QuadTree in terms of range query execution time by a high order of magnitude.     

Querying imprecise data in moving object environments

In moving object environments, it is infeasible for the database tracking the movement of objects to store the exact locations of objects at all times. Typically, the location of an object is known with certainty only at the time of the update. The uncertainty in its location increases until the next update. In this environment, it is possible for queries to produce incorrect results based upon old data. However, if the degree of uncertainty is controlled, then the error of the answers to queries can be reduced. More generally, query answers can be augmented with probabilistic estimates of the validity of the answer. We study the execution of probabilistic range and nearest-neighbor queries. The imprecision in answers to queries is an inherent property of these applications due to uncertainty in data, unlike the techniques for approximate nearest-neighbor processing that trade accuracy for performance. Algorithms for computing these queries are presented for a generic object movement model and detailed solutions are discussed for two common models of uncertainty in moving object databases. We study the performance of these queries through extensive simulations.     

一种预测性连续时空区域查询处理方法

预测性连续时空区域查询在用户指定的时间范围期间持续地返回给定未来查询时间范围期间将出现在查询区域的移动对象。论文提出了一种预测性连续时空区域查询处理方法,设计了支持连续查询处理的两种索引结构。移动对象索引用于记录移动对象不断更新的位置信息,它用于支持查询的首次处理。连续查询索引结构用于记录所有查询结果可能受到移动对象位置变化影响的连续查询,它用于支持连续查询处理。实验表明,论文提出的方法能够有效地提高处理大量连续查询的效率。     

一种适合于频繁位置更新的网络受限移动对象轨迹索引

移动对象索引是支持海量移动对象管理的一项关键技术.目前的移动对象时空轨迹索引方法如STR-Tree、TB-Tree、FNR-Tree、MON-Tree等均直接以轨迹单元作为基本的索引记录单位,在位置更新时需要频繁地在索引中插入新的记录,从而严重地影响了数据库的总体性能.为了解决上述问题,文中提出一种网络受限移动对象的动态概略化轨迹R树索引(DSTR-Tree).DSTR-Tree将索引空间划分成等距格栅,并通过格栅单元对每一条移动对象轨迹进行概略化,然后以概略化轨迹单元为基本索引记录单位建立R树索引.由于概略化轨迹的粒度大大粗于原始轨迹,因此移动对象不需要在每次位置更新的同时触发索引更新,而仅需要在轨迹跨越当前格栅单元时才进行索引更新,从而显著地降低了索引更新的代价.实验结果表明,DSTR-Tree在移动对象数据库频繁位置更新的实际运行条件下,提供了良好的索引维护及总体查询处理性能.     

GPU环境下加速移动对象可视化更新的方法研究

移动对象数据具有规模大、更新频繁的特点,对数据可视化具有较高的性能要求。当数据规模增大时,实时加载数据进行可视化的性能效率会随之降低。为了提高移动对象可视化的效率,提出了GPU环境下的移动对象更新方法,并结合移动对象特征设计出并行查询方案。同时,优化了移动对象的更新函数,通过比较临近的两次可视化查询的时间区间,找出需要更新的时间片,对其进行相应的更新,从而避免了整个时间区间的更新。实验使用了数据规模为400万到1?000万的合成数据集,和包含约960万个采样点的真实出租车数据集。实验结果表明,与CPU上的R-Tree查询、GPU上的R-Tree查询和CPU上更新函数中的串行索引查询方法相比,所提方法具有较好的查询性能,加速比最高可达18.48。移动对象更新函数优化后,当临近的两次可视化查询时间区间完全重叠时,加速效率接近100%。     

A threshold-based algorithm for continuous monitoring of k nearest neighbors

Assume a set of moving objects and a central server that monitors their positions over time, while processing continuous nearest neighbor queries from geographically distributed clients. In order to always report up-to-date results, the server could constantly obtain the most recent position of all objects. However, this naive solution requires the transmission of a large number of rapid data streams corresponding to location updates. Intuitively, current information is necessary only for objects that may influence some query result (i.e., they may be included in the nearest neighbor set of some client). Motivated by this observation, we present a threshold-based algorithm for the continuous monitoring of nearest neighbors that minimizes the communication overhead between the server and the data objects. The proposed method can be used with multiple, static, or moving queries, for any distance definition, and does not require additional knowledge (e.g., velocity vectors) besides object locations.     

不确定移动对象概率Skyline集的查询更新

Skyline查询的研究已从传统的静态Skyline操作延伸到动态的、不确定数据集上的Skyline查询和计算上。研究了移动环境下,查询点位置固定、目标点处于运动状态并且位置不确定情况下的连续概率Skyline计算问题。这个过程中,移动对象与查询对象之间的距离随时间不断变化。移动对象由于其运动状态导致位置无法精确定位,因此移动对象之间的支配关系只能采用概率形式表示,且随时间不断变化。给出了移动对象间的支配概率的定义,以及移动对象Skyline概率的定义,并定义了触发事件来记录对象支配概率发生变化的时刻,实现概率Skyline计算的连续跟踪和动态更新。提出了基于事件触发的连续概率Skyline查询算法(event triggered continuous probabilistic Skyline query for uncertain moving object,U-ECPS),对移动环境下的Skyline集进行连续查询和更新。大量的实验结果验证了U-ECPS算法的有效性。     

曼哈顿路网中移动数据点的连续概率Skyline查询

Skyline queries are widely used in location-based service systems. In recent years, various application queries in Manhattan road network have received considerable attention. Considering the uncertain continuous movement of objects in road networks, we mainly research the problem of continuous probabilistic Skyline queries for uncertain moving data points in Manhattan road networks. In such queries, the query point is considered to be stationary, and the objects in road network are treated as moving data points, which are described by the probability density function. First, we acquire the initial Skyline result set according to the initial location and static attributes of the data points, then, calculate the events that could cause the Skyline result set to change by the domination relations among those moving data points, and at last, update the probability Skyline result set according to the calculated events order so as to achieve continuous probability Skyline query. Experimental results show the efficiency and effectiveness of our proposed methods.     

一种基于P2P的移动对象查询框架

近年来,时空数据查询方法的研究成为人们普遍关注的研究热点.但大部分研究主要集中在集中式环境,在分布式环境下对海量时空数据进行高效的轨迹查询和窗口查询是一件十分有意义且具有挑战性的工作.设计了一种基于P2P的解决方案,提出了对移动对象运动空间进行双层划分的方法来同时支持两种查询.应用网格过滤技术有效地解决了数据频繁更新的问题.对运动空间进行高效的划分,具有比空间填充曲线方法更好的负载平衡性,同时设计了高效的Overlay--SmartChord来支持窗口查询.实验结果表明,和现有方案相比所提方案可以有效减少更新通信量,负载平衡性和路由效率有显著提高.     

Round-Eye: A system for tracking nearest surrounders in moving object environments

This paper presents “Round-Eye”, a system for tracking nearest surrounding objects (or nearest surrounders) in moving object environments. This system provides a platform for surveillance applications. The core part of this system is continuous nearest surrounder (NS) query that maintains views of the nearest objects at distinct angles from query points. This query differs from conventional spatial queries such as range queries and nearest neighbor queries as NS query considers both distance and angular aspects of objects with respect to a query point at the same time. In our system framework, a centralized server is dedicated (1) to collect location updates of both objects and queries, (2) to determine which NS queries are invalidated in presence of object/query location changes and corresponding result changes if any, and (3) to refresh the affected query answers. To enhance the system performance in terms of processing time and network bandwidth consumption, we propose various techniques, namely, safe region, partial query reevaluation, and incremental query result update. Through simulations, we evaluate our system with the proposed techniques over a wide range of settings.     

基于层次组织位置数据库的连续位置相关查询处理

1 引言现有的数据库系统一般假设数据在未被显式修改前是不变的,例如:如果字段salary的值是30.000,那么只有通过事务更新才会改变该字段的值。但对连续变化的对象,如移动对象的位置,应用传统的数据库管理系统来管理会造成两种结果:或者移动对象位置的频繁更新占用大量的系统资源;或者使用移动对象过时的位置信息而导致错误的决策。     

可伸缩的增量连续k近邻查询处理

针对基于TPR树(time-parameterized R-tree)索引的大量并发CKNN(continuous k-nearest neighbor)查询处理,提出了一种可伸缩的增量连续k近邻查询处理(scalable processing of incremental continuous k-nearest neighbor queries,简称SI-CNN)框架,通过引入搜索区域进行预裁剪以减少查询更新所需要的TPR树节点访问代价,并引入了增量结果表以保存候选对象,批量地更新查询结果集,具有良好的可伸缩性.基于SI-CNN框架提出了一种增量更新的SI-CNN查询处理算法,能够基于上次查询结果增量的更新查询,支持查询集合中加入或删除查询和移动对象数据集的插入、删除等动态更新操作.实验结果与分析表明,基于SI-CNN框架的SI-CNN算法可以很好地支持大量并发的CKNN查询处理,具有良好的实用价值.     

Direction-aware KNN queries for moving objects in a road network

Recently more and more people focus on k-nearest neighbor (KNN) query processing over moving objects in road networks, e.g., taxi hailing and ride sharing. However, as far as we know, the existing k-nearest neighbor (KNN) queries take distance as the major criteria for nearest neighbor objects, even without taking direction into consideration. The main issue with existing methods is that moving objects change their locations and directions frequently over time, so the information updates cannot be processed in time and they run the risk of retrieving the incorrect KNN results. They may fail to meet users’ needs in certain scenarios, especially in the case of querying k-nearest neighbors for moving objects in a road network. In order to find the top k-nearest objects moving toward a query point, this paper presents a novel algorithm for direction-aware KNN (DAKNN) queries for moving objects in a road network. In this method, R-tree and simple grid are firstly used as the underlying index structure, where the R-tree is used for indexing the static road network and the simple grid is used for indexing the moving objects. Then, it introduces the notion of “azimuth” to represent the moving direction of objects in a road network, and presents a novel local network expansion method to quickly judge the direction of the moving objects. By considering whether a moving object is moving farther away from or getting closer to a query point, the object that is definitely not in the KNN result set is effectively excluded. Thus, we can reduce the communication cost, meanwhile simplify the computation of moving direction between moving objects and query point. Comprehensive experiments are conducted and the results show that our algorithm can achieve real-time and efficient queries in retrieving objects moving toward query point in a road network.

     

Continuous monitoring of range spatial keyword query over moving objects

In this paper, we propose an efficient solution for processing continuous range spatial keyword queries over moving spatio-textual objects (namely, CRSK-mo queries). Major challenges in efficient processing of CRSK-mo queries are as follows: (i) the query range is determined based on both spatial proximity and textual similarity; thus a straightforward spatial proximity based pruning of the search space is not applicable as any object far from a query location with a high textual similarity score can still be the answer (and vice versa), (ii) frequent location updates may invalidate a query result, and thus require frequent re-computing of the result set for any object updates. To address these challenges, the key idea of our approach is to exploit the spatial and textual upper bounds between queries and objects to form safe zones (at the client-side) and buffer regions (at the server-side), and then use these bounds to quickly prune objects and queries through smart in-memory data structures. We conduct extensive experiments with a synthetic dataset that verify the effectiveness and efficiency of our proposed algorithm.