基于直方图聚类技术的海量数据值域查询

随着现代科技和传感器的发展和应用,复杂多变的空间数据日益膨胀。为了有效地使用这些海量数据,不仅需要搜索元数据而且包括实际数据。要想通过扫描这些海量数据来回答值域查询显而易见是不现实的。该文研究了一种数据直方图聚类技术,用于栅格地球科学数据值域查询。实验表明,该方法不仅可以快速近似地回答统计范围查询,同时可以给出准确评价。     

动态数据立方的范围查询

根据data cube层次性的特点和查询习惯提出了新的分块计算方法，并在此基础上提出了改进算法．这种方法节约了存储空间，在LBD粒度及其上的查询效率为O(1)，同时数据的更新时间大约为O()，还节约了大量的存储空间，并且使得数据立方具有了一定的结构独立性，能有效的减少重新构造数据立方(reprocess)的次数，因而在时间上和效率上有较大的优势．     

On Cartesian Trees and Range Minimum Queries

We present new results on Cartesian trees with applications in range minimum queries and bottleneck edge queries. We introduce a cache-oblivious Cartesian tree for solving the range minimum query problem, a Cartesian tree for the bottleneck edge query problem on trees and undirected graphs, and a proof that no Cartesian tree exists for the two-dimensional version of the range minimum query problem.     

道路网中的移动对象连续范围查询

研究了采用网络距离的道路网上移动对象连续多范围查询处理技术。设计了道路网、移动对象和查询数据在内存中存储的数据模型。基于该数据模型提出了两种道路网上的移动对象连续多范围查询处理算法。其中,增量式范围查询算法(incremental range query algorithm,IRQA)通过使用扩张树和影响列表结构减少查询的重新计算;组范围查询算法(group range query algorithm,GRQA)利用同一路径上多查询的结果具有相关性这一特点减少查询的重新计算。实验结果表明GRQA算法在查询分布比较集中时性能较优,IRQA算法在查询均匀分布时性能较优,此外,两种算法均优于重新计算所有查询结果的原始算法。     

基于本地差分隐私的空间范围查询方法

基于本地差分隐私的用户数据收集与分析得到了研究者的广泛关注.用户数据的值域大小、编码机制以及扰动机制直接制约着空间范围查询的精度.针对现有编码机制与扰动机制难以有效响应空间范围查询的不足,提出了一种基于网格分割与四分树索引的空间范围查询响应方法GT-R(grid-based quadtree range query),该方法利用网格对用户数据的值域进行均匀分割,产生大小均等的单元格区域.同时利用四分树结构对所有单元格区域进行索引.每个用户结合服务器共享的四分树副本,对所拥有的数据进行编码.借助于编码后的四分树进行层次随机采样,并利用优化随机应答机制对所采层次中的结点进行本地扰动处理.服务器利用每个用户的报告值重构四分树索引结构,并响应空间范围查询.GT-R与现有的编码机制与扰动机制在真实的大规模空间数据集上实验结果表明,其分割精度以及响应范围查询效果优于同类算法.     

局部范围受限的多类型最近邻查询

多类型最近邻查询在现实中的应用范围比传统的最近邻查询广泛.基于多类型最近邻查询,提出局部范围受限的多类型最近邻查询(PCMTNN)概念,针对范围约束是任意简单多边形区域的数据集给出PCMTNN算法,利用椭圆最小外切矩形的易求性和与椭圆本身覆盖区域的最近似性特点缩小了搜索范围,并用一个链表结构实现了在一次R树的遍历过程中找到包含在所有搜索区域内的数据集中点的过程,从而大幅度减少了无用点的访问数量.实验结果分析表明算法具有较好的性能.     

Fuzzy Distance-Based Range Queries over Uncertain Moving Objects

Data obtained from real world are imprecise or uncertain due to the accuracy of positioning devices,updating protocols or characteristics of applications.On the other hand,users sometimes prefer to qualitatively express their requests with vague conditions and different parts of search region are in-equally important in some applications.We address the problem of efficiently processing the fuzzy range queries for uncertain moving objects whose whereabouts in time are not known exactly,for which the basic syntax is find objects always/sometimes near to the query issuer with the qualifying guarantees no less than a given threshold during a given temporal interval.We model the location uncertainty of moving objects on the utilization of probability density functions and describe the indeterminate boundary of query range with fuzzy set.We present the qualifying guarantee evaluation of objects,and propose pruning techniques based on the α-cut of fuzzy set to shrink the search space efficiently.We also design rules to reject non-qualifying objects and validate qualifying objects in order to avoid unnecessary costly numeric integrations in the refinement step.An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of algorithms under various experimental     

Uncertain Distance-Based Range Queries over Uncertain Moving Objects

Distance-based range search is crucial in many real applications. In particular, given a database and a query issuer, a distance-based range search retrieves all the objects in the database whose distances from the query issuer are less than or equal to a given threshold. Often, due to the accuracy of positioning devices, updating protocols or characteristics of applications (for example, location privacy protection), data obtained from real world are imprecise or uncertain. Therefore, existing approaches over exact databases cannot be directly applied to the uncertain scenario. In this paper, we redefine the distance-based range query in the context of uncertain databases, namely the probabilistic uncertain distance-based range (PUDR) queries, which obtain objects with confidence guarantees. We categorize the topological relationships between uncertain objects and uncertain search ranges into six cases and present the probability evaluation in each case. It is verified by experiments that our approach outperform Monte-Carlo method utilized in most existing work in precision and time cost for uniform uncertainty distribution. This approach approximates the probabilities of objects following other practical uncertainty distribution, such as Gaussian distribution with acceptable errors. Since the retrieval of a PUDR query requires accessing all the objects in the databases, which is quite costly, we propose spatial pruning and probabilistic pruning techniques to reduce the search space. Two metrics, false positive rate and false negative rate are introduced to measure the qualities of query results. An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.     

Disk Allocation for Fast Range and Nearest-Neighbor Queries

As databases increasingly integrate non-textual multimedia information it is becoming necessary to support efficient similarity searching in addition to range searching. Range and nearest-neighbor (similarity) queries are the most important class of queries for multimedia and multi-dimensional databases. Due to the large sizes of the datasets involved, I/O is a critical factor limiting performance. The use of parallel I/O through declustering of the data is a promising approach to improve performance. Consequently several research efforts have addressed the problem of declustering multidimensional data for optimizing range and partial match queries. Very limited work has been done for similarity queries, and the problem of declustering for combined range and similarity queries has not been addressed in the literature. Consider a dataset of images where the following metadata for each image is also stored: date on which the picture was taken, longitudeand latitude of the site of the picture. An example of a combined query is: Given a target image, find the 5 most similar images taken within 3 months of the target image and located within 2 degrees of longitude and latitude of the target image. In order to answer this query, it is necessary to conduct a range search on the date, longitude and latitude values and a similarity search on the image content.In this paper, we develop new declustering schemes that provide good declustering for similarity searching. In addition, we show that the new schemes have very good performance for range queries as well as combination queries. The new schemes are based upon the Cyclic declustering schemes which were developed for range and partial match queries. The Cyclic schemes not only provide superior performance to earlier schemes, but are also very robust and consistent with respect to query types and variations in system parameters.     

基于MapReduce模型的范围查询分析优化技术研究

近年来,MapReduce并行计算模型受到工业界和学术界广泛关注.基于该模型的系统实现已在谷歌、雅虎、Facebook等大公司内部成功应用.然而,基于MapReduce的系统实现最初用于解决海量无结构、半结构化数据的批处理问题,例如生成倒排索引、计算网页的pagerank、日志分析等,在设计上缺乏针对海量结构化数据进行交互式分析处理的优化考虑,例如：它总是采用全数据集强力扫描的数据处理模式,这有悖于结构化数据管理中常用的操作模式——选择性查询分析处理.针对该问题,引入传统数据库管理领域中常用的全局索引技术,将其应用在基于MapReduce模型的开源项目Hadoop上,以block为粒度对Hadoop分布式文件系统上的结构化数据构建全局索引结构,并给出一种面向范围查询分析的作业编译与调度执行优化算法,主要目标是基于应用语义及辅助索引结构减少不必要的map任务数,进而优化作业的调度开销和执行开销.在实验验证阶段,给出了80%,50%,30%,10%四种数据选择率在3种集群规模下的优化效果,发现作业响应时间最高可提升5倍,I/O开销最高提升10倍,任务调度开销最高提升11倍.     

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     

适用于范围查询的列存储数据桶划分算法

范围查询是数据库中一项重要的操作.列存储数据库中,能否有效查找一个范围内的属性值,获取对应的行号集合,将极大影响元组重构的效率.与树型结构相比,Hash表对数据的精确查找具有更高的效率,但是范围查找的效率比较低.针对这种情况,提出了一种改进的可用于范围查询的数据桶划分算法.为了能够更好地对算法进行描述,首先提出了可用于范围查询的Hash存储模型(ranged Hash,RH),并给出了桶的值域和序列化的定义.其次针对列存储等“读优先”特性,在RH模型的基础上,提出一种改进的桶划分算法.该算法生成可序列化的哈希函数把属性值划分到桶中,能够同时提高属性值的范围查询效率和存储效率.最后,通过实验结果验证算法的有效性.     

不确定数据上范围受限的最近邻查询算法

不确定数据的查询处理是数据库领域近年来的热点研究课题.提出一种不确定数据上的范围受限的最近邻查询.给定不确定数据集D={o1,o2,…,on},范围约束R是一个简单多边形,q为一固定的查询点,范围受限的最近邻查询返回的是在数据集D中,既满足范围约束R,又能成为查询点q的最近邻的对象集合.为处理该查询,提出了范围受限的最近邻核心集的概念和范围受限的最近邻核心集的查找算法.并提出一种计算范围受限的最近邻候选集的优化方法,降低了查询代价.最后通过实验验证了该算法的有效性.     

Efficient Processing of Continual Range Queries for Location-Aware Mobile Services

Efficient processing of continual range queries is important in providing location-aware mobile services. In this paper, we study a new main memory-based approach to indexing continual range queries to support location-aware mobile services. The query index is used to quickly answer the following question continually: “Which moving objects are currently located inside the boundaries of individual queries?” We present a covering tile-based (COVET) query index. A set of virtual tiles are predefined, each with a unique ID. One or more of the virtual tiles are used to strictly cover the region defined by an individual range query. The query ID is inserted into the ID lists associated with the covering tiles. These covering tiles touch each other only at the edges. A COVET index maintains a mapping between a covering tile and all the queries that contain that tile. For any object position, search is conducted indirectly via the covering tiles. More importantly, a COVET-based query index allows query evaluation to take advantage of incremental changes in object locations. Computation can be saved for those objects that have not moved outside the boundaries of covering tiles. Simulations are conducted to evaluate the effectiveness of the COVET index and compare virtual tiles of different shapes and sizes.     

路网中空间关键字连续范围查询算法研究

空间关键字查询相对传统的位置相关查询而言更能满足实际查询处理的需要。着重探讨路网中结合距离和关键字相似度两个因素的空间关键字查询处理问题,提出解决路网中空间关键字连续范围查询(CRSKQ)的有效方法。提出了一个综合考虑了路网上的道路、对象和路网的连通性的路网模型以支持CRSKQ查询的处理。为了实现连续监控,所提出的算法包括两个阶段,即初始结果获取和查询结果连续监控。初始结果监控阶段,通过路网扩展和关键字匹配寻找满足要求的结果对象;在连续监控阶段,充分利用前面时刻的查询结果来减小连续监控的代价。模拟实验表明,所提出的算法是有效的。     

Evaluation of Queries under Closed-World Assumption

The minimal entailment Min has been characterized elsewhere by where Cn is the first-order consequence operation, P is a set of clauses (indefinite deductive data base; in short: a data base), is a clause (a query), and Pos is the set of positive (that is, bodiless) ground clauses. In this paper, we address the problem of the computational feasibility of criterion (1). Our objective is to find a query evaluation algorithm that decides P Min by what we call indefinite modeling, without actually computing all ground positive consequences of P and P {}. For this purpose, we introduce the concept of minimal indefinite Herbrand model MP of P, which is defined as the set of subsumption-minimal ground positive clauses provable from P. The algorithm first computes MP by finding the least fixed-point of an indefinite consequence operator TIP. Next, the algorithm verifies whether every ground positive clause derivable from MP {} by one application of the parallel positive resolution rule (in short: the PPR rule) is subsumed by an element of MP. We prove that the PPR rule, which can derive only positive clauses, is positively complete, that is, every positive clause provable from a data base P is derivable from P by means of subsumption and finitely many applications of PPR. From this we conclude that the presented algorithm is partially correct and that it eventually halts if both P and MP are finite. Moreover, we indicate how the algorithm can be modified to handle data bases with infinite indefinite Herbrand models. This modification leads to a concept of universal model that allows for nonground clauses in its Herbrand base and appears to be a good candidate for representation of indefinite deductive data bases.     

时间区间上的不确定移动对象距离范围查询

针对目标对象与查询发出者皆为不确定移动对象的情况,提出了一种时间区间上的距离范围查询（DRqTI）.此类查询搜索出数据集中在给定时间区间内,到查询发出者距离不超过阈值的目标对象,查询结果中包含对象满足查询条件的有效时间段和匹配度.提出了基于轨迹、基于时间区间和基于距离的三种剪枝策略,并给出了精炼和匹配度计算方法,在此基础上设计了查询处理算法.实验分析表明,三种剪枝策略中基于距离的方法性能最佳,提出的算法能有效处理DRqTI问题.