Incremental Processing of Continual Range Queries over Moving Objects

Efficient processing of continual range queries over moving objects is critically important in providing location-aware services and applications. A set of continual range queries, each defining the geographical region of interest, can be periodically (re)evaluated to locate moving objects that are currently within individual query boundaries. We study a new query indexing method, called CES-based indexing, for incremental processing of continual range queries over moving objects. A set of containment-encoded squares (CES) are predefined, each with a unique ID. CESs are virtual constructs (VC) used to decompose query regions and to store indirectly precomputed search results. Compared with a prior VC-based approach, the number of VCs visited in a search operation is reduced from (4L²-1)/3 to log(L)+1, where L is the maximal side length of a VC. Search time is hence significantly lowered. Moreover, containment encoding among the CESs makes it easy to identify all those VCs that need not be visited during an incremental query (re)evaluation. We study the performance of CES-based indexing and compare it with a prior VC-based approach     

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.     

利用覆盖区域设计与实现移动对象索引

对移动对象索引频繁更新问题进行了研究,提出了一种基于区域覆盖的空间索引结构虚拟网格四分树(virtual grid quadtree,VGQ);通过索引移动对象所在的区域而非移动对象本身来减少由于移动对象位置的改变而引起的索引结构的改变,并给出了近似连续范围查询算法及增量和自底向上优化策略。实验结果表明,VGQ在查询效率和空间使用上是一种有效的索引方法。     

Density-based spatial keyword querying

With the rocket development of the Internet, WWW(World Wide Web), mobile computing and GPS (Global Positioning System) services, location-based services like Web GIS (Geographical Information System) portals are becoming more and more popular. Spatial keyword queries over GIS spatial data receive much more attention from both academic and industry communities than ever before. In general, a spatial keyword query containing spatial location information and keywords is to locate a set of spatial objects that satisfy the location condition and keyword query semantics. Researchers have proposed many solutions to various spatial keyword queries such as top-K keyword query, reversed kNN keyword query, moving object keyword query, collective keyword query, etc. In this paper, we propose a density-based spatial keyword query which is to locate a set of spatial objects that not only satisfies the query’s textual and distance condition, but also has a high density in their area. We use the collective keyword query semantics to find in a dense area, a group of spatial objects whose keywords collectively match the query keywords. To efficiently process the density based spatial keyword query, we use an IR-tree index as the base data structure to index spatial objects and their text contents and define a cost function over the IR-tree indexing nodes to approximately compute the density information of areas. We design a heuristic algorithm that can efficiently prune the region according to both the distance and region density in processing a query over the IR-tree index. Experimental results on datasets show that our method achieves desired results with high performance.     

VGQ-Vor: extending virtual grid quadtree with Voronoi diagram for mobile k nearest neighbor queries over mobile objects

Performing mobile k nearest neighbor (MkNN) queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried aremobile. The performance of this kind of query relies heavily on the maintenance of the current locations of the objects. The index used for mobile objects must support efficient update operations and efficient query handling. This study aims to improve the performance of the MkNN queries while reducing update costs. Our approach is based on an observation that the frequency of one region changing between being occupied or not by mobile objects is much lower than the frequency of the position changes reported by the mobile objects. We first propose an virtual grid quadtree with Voronoi diagram(VGQ-Vor), which is a two-layer index structure that indexes regions occupied by mobile objects in a quadtree and builds a Voronoi diagram of the regions. Then we propose a moving k nearest neighbor (kNN) query algorithm on the VGQ-Vor and prove the correctness of the algorithm. The experimental results show that the VGQ-Vor outperforms the existing techniques (Bx-tree, Bdual-tree) by one to three orders of magnitude in most cases.     

分布式环境下大规模移动对象范围查询算法

移动对象的连续范围查询是许多基于位置的服务的核心问题。针对该问题,提出一种面向大规模移动对象并发范围查询的分布式搜索方法。首先,设计了一种由全局网格索引（GGI）和局部弹性四叉树构成的移动对象分布式动态索引（DDI）结构。其次,提出了一种基于DDI结构的分布式查询算法（DSA）,该算法首先引入了一种在移动对象和查询点的位置连续变化的情况下的查询结果增量更新策略;然后,在增量更新过程中引入一种面向多并发查询的共享计算优化策略,该策略能够根据已有计算结果对移动对象范围查询结果进行增量搜索。最后,基于德国路网模拟了3个具有不同空间分布的移动对象数据集,将DSA与NS(Naive Search)、GI(Grid Index)和分布式混合索引（DHI）进行对比。实验结果表明,与性能最好的对比算法DHI相比,DSA的初始查询时间减少了22.7%,增量查询时间减少了15.2%,性能优于对比算法。     

基于广播空间数据安全查询方法

索引提供了选择性调优,为了节约电池,客户需等待并调优到最佳索引段。在位置感知移动服务里(LAMSs),由于晚查询响应可能包含过时的信息,重要的是要减少查询响应时间。因此提出一个基于广播空间查询方案,处理方案旨在支持最邻近查询处理。分配方案中,广播数据项按顺序进行排序,基于其位置和客户可以选择性地调整到所需的数据项,而不需要一个索引段。由此产生的延迟和调优时间已经接近最优值,作为分析和仿真结果显示。     

Exploiting hashing for concurrent query processing and indexing of current location of moving objects

Due to recent developments in location-based services and mobile computing, the need for indices for moving objects has been strengthened to improve the response time of a query operation. With a single index in place for managing both the update and query operations for moving objects, the index needs to be updated each time the object moves. This deteriorates the performance of concurrent query operations. It is critical to handle the conflicts between continuous update and query operations effectively using appropriate concurrency control protocol otherwise, inconsistent results will be reported. Many indices have been proposed in the literature for moving objects but they lack the support for processing concurrent operations. Further, the consistent indices in the literature are based on tree structure that have computationally expensive split/merge operations that can negatively affect the response time of query processing algorithms. Moreover such tree based indices are proposed for historical and future timeline data. As the scope of this article is on current timeline and concurrent continuous query operations, therefore, we exploit state of the art hash-based indices in the literature and presented the two consistent versions. The comparative analysis of the indices is performed and meaningful findings are also presented along.     

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。     

Continuous Query Processing of Spatio-Temporal Data Streams in PLACE

The tremendous increase in the use of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous query processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. These environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous query processor includes: (1) New incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extended semantics of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) A shared-execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Experimental evaluation shows promising performance of the continuous query processor of the PLACE server. This work was supported in part by the National Science Foundation under Grants IIS-0093116, IIS-0209120, and 0010044-CCR.     

Spatial query processing for fuzzy objects

Range and nearest neighbor queries are the most common types of spatial queries, which have been investigated extensively in the last decades due to its broad range of applications. In this paper, we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS communities. Existing research on fuzzy objects mainly focuses on modeling basic fuzzy object types and operations, leaving the processing of more advanced queries largely untouched. In this paper, we propose two new kinds of spatial queries for fuzzy objects, namely single threshold query and continuous threshold query, to determine the query results which qualify at a certain probability threshold and within a probability interval, respectively. For efficient single threshold query processing, we optimize the classical R-tree-based search algorithm by deriving more accurate approximations for the distance function between fuzzy objects and the query object. To enhance the performance of continuous threshold queries, effective pruning rules are developed to reduce the search space and speed up the candidate refinement process. The efficiency of our proposed algorithms as well as the optimization techniques is verified with an extensive set of experiments using both synthetic and real datasets.     

面向不确定移动对象的连续K近邻查询算法*

近年来,位置服务等领域急需解决的一个难点问题是不确定移动对象连续K近邻查询.基于此情况,文中提出高效的面向不确定移动对象的连续K近邻查询算法.首先提出2种预测移动对象可能区域算法MaxMin与Rate,利用最近一段时间窗口内的位置采样、速度和方向预测移动对象在查询时刻到未来I区间可能的位置区域.同时使用最小距离与最大距离区间描述移动对象到查询对象的距离.然后采用优化的基于模糊可能度判定的排序方法查找查询对象的K近邻.最后在真实和合成的大规模移动对象数据集上验证文中方法的有效性.     

A concurrently updatable index structure for predicted paths of moving objects

While location-aware services, both in professional and private context, are widely used today, not all the available knowledge is exploited. The predicted path moving objects follow when being guided e.g. by a smartphone, is not used, instead only the current position is taken into account. In this article, we describe how the exploitation of not only point but also route information can be used to offer a new value to the users as well as a reduction of resource usage. It allows having queries on the future position of moving objects, taking the probability of its estimation into account. An index structure to handle this kind of objects and queries is introduced. Different methods for updating objects are proposed, allowing for even less consumption of resources. A set of experiments shows the performance of our approach. This enables new services to be able to cope with a growing demand, both in quality and quantity, by using sophisticated algorithms and return real value to the user. Furthermore, the communication between server and client is optimized to avoid an overload of the mobile network.     

A pure peer-to-peer approach for kNN query processing in mobile ad hoc networks

Together with advanced positioning and mobile technologies, P2P query processing has attracted a growing interest number of location-aware applications such as answering kNN queries in mobile ad hoc networks. It not only overcomes drawbacks of centralized systems, for example single point of failure and bottleneck issues, but more importantly harnesses power of peers’ collaboration. In this research, we propose a pure mobile P2P query processing scheme which primarily focuses on the search and validation algorithm for kNN queries. The proposed scheme is designed for pure mobile P2P environments with the absence of the base station support. Compared with centralized and hybrid systems, our system can reduce energy consumption more than six times by making use of data sharing from peers in a reasonable mean latency of processing time for networks with high density of moving objects as can be seen in the simulation results.     

Peer-to-peer bichromatic reverse nearest neighbours in mobile ad-hoc networks

The increasing use of mobile communications has raised many issues of decision support and resource allocation. A crucial problem is how to solve queries of Reverse Nearest Neighbour (RNN). An RNN query returns all objects that consider the query object as their nearest neighbour. Existing methods mostly rely on a centralised base station. However, mobile P2P systems offer many benefits, including self-organisation, fault-tolerance and load-balancing. In this study, we propose and evaluate 3 distinct P2P algorithms focusing on bichromatic RNN queries, in which mobile query peers and static objects of interest are of two different categories, based on a time-out mechanism and a boundary polygon around the mobile query peers. The Brute-Force Search Algorithm provides a naive approach to exploit shared information among peers whereas two other Boundary Search Algorithms filter a number of peers involved in query processing. The algorithms are evaluated in the MiXiM simulation framework with both real and synthetic datasets. The results show the practical feasibility of the P2P approach for solving bichromatic RNN queries for mobile networks.     

基于位置服务中的连续查询隐私保护研究

近年来,伴随着移动计算技术和无限设备的蓬勃发展,位置服务中的隐私保护研究受到了学术界的广泛关注,提出了很多匿名算法以保护移动用户的隐私信息.但是现有方法均针对snapshot查询,不能适用于连续查询.如果将现有的静态匿名算法直接应用于连续查询,将会产生隐私泄露、匿名服务器工作代价大等问题.针对这些问题,提出了δp-隐私模型和δq-质量模型来均衡隐私保护与服务质量的矛盾,并基于此提出了一种贪心匿名算法.该算法不仅适用于snapshot查询,也适用于连续查询.实验结果证明了算法的有效性.     

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.     

Probabilistic nearest neighbor query processing on distributed uncertain data

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.     

An Indexing Method for Answering Queries on Moving Objects

We consider the problem of indexing a set of objects moving in d-dimensional spaces along linear trajectories. A simple external-memory indexing scheme is proposed to efficiently answer general range queries. The following are examples of the queries that can be answered by the proposed method: report all moving objects that will (i) pass between two given points within a specified time interval; (ii) become within a given distance from some or all of a given set of other moving objects. Our scheme is based on mapping the objects to a dual space, where queries about moving objects are transformed into polyhedral queries concerning their speeds and initial locations. We then present a simple method for answering such polyhedral queries, based on partitioning the space into disjoint regions and using a B+-tree to index the points in each region. By appropriately selecting the boundaries of each region, we guarantee an average search time that matches a known lower bound for the problem. Specifically, for a fixed d, if the coordinates of a given set of N points are statistically independent, the proposed technique answers polyhedral queries, on the average, in O((N/B)^1−1/d⋅(log _B N)^1/d+K/B) I/O's using O(N/B) space, where B is the block size, and K is the number of reported points. Our approach is novel in that, while it provides a theoretical upper bound on the average query time, it avoids the use of complicated data structures, making it an effective candidate for practical applications. The proposed index is also dynamic in the sense that it allows object insertion and deletion in an amortized update cost of log _B(N) I/O's. Experimental results are presented to show the superiority of the proposed index over other methods based on R-trees. recommend Ahmed Elmagarmid     

多媒体数据库的时态索引技术

针对多媒体数据库中具有时态特性的媒体对象，提出一种基于区间的索引机制，目的是为了提高时间检索的性能。这种时态索引可以快速查找任意时间段中的时态对象，它能有效地支持多种时态关系操作符；支持多种时态集函数；支持时态ＪＯＩＮ操作等等。该索引支持媒体展示过程。