Development operators on 4D moving object databases

In this paper we propose four dimensional (4D) operators, which can be used to deal with sequential changes of topological relationships between 4D moving objects and we call them 4D development operators. In contrast to the existing operators, we can apply the operators to real applications on 4D moving objects. We also propose a new approach to define them. The approach is based on a dimension separated method, which considers x y coordinates and z coordinates separately. In order to show the applicability of our operators, we show the algorithms for the proposed operators and development graph between 4D moving objects.

The Discrete Representation of Continuously Moving Indeterminate Objects

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.

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. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed update strategies.

时空数据库中的时空拓扑查询

1引言 现代社会大量的科学应用,如航空系统的飞行器导航、森林火灾探测、乃至于人们生活中应用越来越广泛的移动通讯手机等等,这些都导致有必要研究一个能兼顾时空两属性的数据库管理系统.研制一个能有效表示和处理时空对象的DBMS,其中一项重要工作就是对时空对象进行空间分析操作.

Adaptive Indexing of Moving Objects with Highly Variable Update Frequencies

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.

基于Oracle Spatial的空间线/面拓扑关系判断的实现

利用Oracle Spatial提供的简单的空间关系查询操作算子,对九交模型所描述的任意两个2维空间不自相交的线目标与面目标(不含空洞)的拓扑关系判断设计了算法,最终进行了试验验证,实现了线目标与面目标间的19种空间拓扑关系的判断。

PRISMO: predictive skyline query processing over moving objects

Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS （branch-and-bound skyline with rescanning and repacking） and TPBBS （time-parameterized branch- and-bound skyline）, each with two alternative methods, to handle predictive skyline computation. The basic TPRBS is further extended to TPBBSE （TPBBS with expansion） to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.

对象关系数据库中的矢量数据集空间叠加方法

首先对当前空间数据模型的特点进行了评述,指出空间数据的存储方式与空间叠加方案的设计与实现密切相关,然后基于当前流行的对象关系数据模型,提出了将拓扑复合数据结构引入对象关系数据模型的空间叠加设计思想,并结合矢量数据空间叠加的流程和对象关系数据库的特点分析了实现该技术思想的关键技术问题,最后以PostgreSQL数据库管理系统为例,设计了可行的矢量数据集空间叠加方案。

Efficient κ-Nearest-Neighbor Search Algorithms for Historical Moving Object Trajectories

Nearest Neighbor （κNN） search is one of the most important operations in spatial and spatio-temporal databases. Although it has received considerable attention in the database literature, there is little prior work on κNN retrieval for moving object trajectories. Motivated by this observation, this paper studies the problem of efficiently processing κNN （κ≥ 1） search on R-tree-like structures storing historical information about moving object trajectories. Two algorithms are developed based on best-first traversal paradigm, called BFPκNN and BFTκNN, which handle the κNN retrieval with respect to the static query point and the moving query trajectory, respectively. Both algorithms minimize the number of node access, that is, they perform a single access only to those qualifying nodes that may contain the final result. Aiming at saving main-memory consumption and reducing CPU cost further, several effective pruning heuristics are also presented. Extensive experiments with synthetic and real datasets confirm that the proposed algorithms in this paper outperform their competitors significantly in both efficiency and scalability.