An approach to process continuous location-dependent queries on moving objects with support for location granules

Location-based services have attracted the attention of important research in the field of mobile computing. Specifically, different mechanisms have been proposed to process location-dependent queries. In the above mentioned context, it is usually assumed that the location data are expressed at a fine geographic precision. However, a different granularity may be more appropriate in certain situations. Thus, a location resolution higher than required may even be inconvenient or not understandable by the user (for example, if the user expects a city name as an answer and instead the system provides the latitude/longitude coordinates). Moreover, if the locations presented to the user need to be refreshed automatically as the objects move, it is obvious that maintaining up-to-date GPS-like geographic coordinates would be more expensive in terms of processing and communication. Unfortunately, the existing approaches assume queries whose locations are always given with maximum precision (i.e., GPS locations).In this paper, a distributed query processing approach that adapts itself to the level of the location resolution required is presented. Thus, it supports continuous location-dependent queries based on the required terminology for the locations, depending on the granularity used (e.g., GPS, cities, states, provinces, or any other predefined geographic area). For the above mentioned purpose, location granules can be defined to specify the semantics appropriate for the queries and/or the way the results should be presented. A prototype showing the functionality and benefits of the approach has been implemented and used in an extensive experimental evaluation. The proposal not only increases the flexibility and expressive power of the queries considerably but also performs efficiently.     

Evaluation of probabilistic queries over imprecise data in constantly-evolving environments

Sensors are often employed to monitor continuously changing entities like locations of moving objects and temperature. The sensor readings are reported to a database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), the database may not be able to keep track of the actual values of the entities. Queries that use these old values may produce incorrect answers. However, if the degree of uncertainty between the actual data value and the database value is limited, one can place more confidence in the answers to the queries. More generally, query answers can be augmented with probabilistic guarantees of the validity of the answers. In this paper, we study probabilistic query evaluation based on uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers, and provide efficient indexing and numeric solutions. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments are performed to examine the effectiveness of several data update policies.     

面向PSTP查询的高效处理算法

在使用"不完全结构的约束查询(PSTP查询)"从XML文档中获取信息时,用户可以根据自身对XML文档结构的熟悉程度,在查询表达式中灵活地嵌入结构约束条件,从而满足完全不了解、完全了解及了解部分结构信息的各种用户的查询需求。提出一种基于扩展Dewey编码的查询处理算法,可以在仅扫描一遍元素的情况下,处理任意形式的PSTP查询。不同数据集上的实验结果表明,EDPS算法在处理twig查询、不包含"*"结点的PSTP查询及包含"*"结点的PSTP查询时,综合性能明显优于已有方法。     

Trip planning queries with location privacy in spatial databases

Privacy has become a major concern for the users of location-based services (LBSs) and researchers have focused on protecting user privacy for different location-based queries. In this paper, we propose techniques to protect location privacy of users for trip planning (TP) queries, a novel type of query in spatial databases. A TP query enables a user to plan a trip with the minimum travel distance, where the trip starts from a source location, goes through a sequence of points of interest (POIs) (e.g., restaurant, shopping center), and ends at a destination location. Due to privacy concerns, users may not wish to disclose their exact locations to the location-based service provider (LSP). In this paper, we present the first comprehensive solution for processing TP queries without disclosing a user’s actual source and destination locations to the LSP. Our system protects the user’s privacy by sending either a false location or a cloaked location of the user to the LSP but provides exact results of the TP queries. We develop a novel technique to refine the search space as an elliptical region using geometric properties, which is the key idea behind the efficiency of our algorithms. To further reduce the processing overhead while computing a trip from a large POI database, we present an approximation algorithm for privacy preserving TP queries. Extensive experiments show that the proposed algorithms evaluate TP queries in real time with the desired level of location privacy.     

Towards fusing uncertain location data from heterogeneous sources

Properly incorporating location-uncertainties – which is, fully considering their impact when processing queries of interest – is a paramount in any application dealing with spatio-temporal data. Typically, the location-uncertainty is a consequence of the fact that objects cannot be tracked continuously and the inherent imprecision of localization devices. Although there is a large body of works tackling various aspects of efficient management of uncertainty in spatio-temporal data – the settings consider homogeneous localization devices, e.g., either a Global Positioning System (GPS), or different sensors (roadside, indoor, etc.).In this work, we take a first step towards combining the uncertain location data – i.e., fusing the uncertainty of moving objects location – obtained from both GPS devices and roadside sensors. We develop a formal model for capturing the whereabouts in time in this setting and propose the Fused Bead (FB) model, extending the bead model based solely on GPS locations. We also present algorithms for answering traditional spatio-temporal range queries, as well as a special variant pertaining to objects locations with respect to lanes on road segments – augmenting the conventional graph based road network with the width attribute. In addition, pruning techniques are proposed in order to expedite the query processing. We evaluated the benefits of the proposed approach on both real (Beijing taxi) and synthetic (generated from a customized trajectory generator) data. Our experiments demonstrate that the proposed method of fusing the uncertainties may eliminate up to 26 % of the false positives in the Beijing taxi data, and up to 40 % of the false positives in the larger synthetic dataset, when compared to using the traditional bead uncertainty models.     

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。     

PrDB: managing and exploiting rich correlations in probabilistic databases

Due to numerous applications producing noisy data, e.g., sensor data, experimental data, data from uncurated sources, information extraction, etc., there has been a surge of interest in the development of probabilistic databases. Most probabilistic database models proposed to date, however, fail to meet the challenges of real-world applications on two counts: (1) they often restrict the kinds of uncertainty that the user can represent; and (2) the query processing algorithms often cannot scale up to the needs of the application. In this work, we define a probabilistic database model, PrDB, that uses graphical models, a state-of-the-art probabilistic modeling technique developed within the statistics and machine learning community, to model uncertain data. We show how this results in a rich, complex yet compact probabilistic database model, which can capture the commonly occurring uncertainty models (tuple uncertainty, attribute uncertainty), more complex models (correlated tuples and attributes) and allows compact representation (shared and schema-level correlations). In addition, we show how query evaluation in PrDB translates into inference in an appropriately augmented graphical model. This allows us to easily use any of a myriad of exact and approximate inference algorithms developed within the graphical modeling community. While probabilistic inference provides a generic approach to solving queries, we show how the use of shared correlations, together with a novel inference algorithm that we developed based on bisimulation, can speed query processing significantly. We present a comprehensive experimental evaluation of the proposed techniques and show that even with a few shared correlations, significant speedups are possible.     

An efficient approach for continuous density queries

In location-based services, a density query returns the regions with high concentrations of moving objects (MOs). The use of density queries can help users identify crowded regions so as to avoid congestion. Most of the existing methods try very hard to improve the accuracy of query results, but ignore query efficiency. However, response time is also an important concern in query processing and may have an impact on user experience. In order to address this issue, we present a new definition of continuous density queries. Our approach for processing continuous density queries is based on the new notion of a safe interval, using which the states of both dense and sparse regions are dynamically maintained. Two indexing structures are also used to index candidate regions for accelerating query processing and improving the quality of results. The efficiency and accuracy of our approach are shown through an experimental comparison with snapshot density queries.     

The optimal sequenced route query

Real-world road-planning applications often result in the formulation of new variations of the nearest neighbor (NN) problem requiring new solutions. In this paper, we study an unexplored form of NN queries named optimal sequenced route (OSR) query in both vector and metric spaces. OSR strives to find a route of minimum length starting from a given source location and passing through a number of typed locations in a particular order imposed on the types of the locations. We first transform the OSR problem into a shortest path problem on a large planar graph. We show that a classic shortest path algorithm such as Dijkstra’s is impractical for most real-world scenarios. Therefore, we propose LORD, a light threshold-based iterative algorithm, which utilizes various thresholds to prune the locations that cannot belong to the optimal route. Then we propose R-LORD, an extension of LORD which uses R-tree to examine the threshold values more efficiently. Finally, for applications that cannot tolerate the Euclidean distance as estimation and require exact distance measures in metric spaces (e.g., road networks) we propose PNE that progressively issues NN queries on different point types to construct the optimal route for the OSR query. Our extensive experiments on both real-world and synthetic datasets verify that our algorithms significantly outperform a disk-based variation of the Dijkstra approach in terms of processing time (up to two orders of magnitude) and required workspace (up to 90% reduction on average).     

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.     

Supporting top-<Emphasis Type="Italic">k</Emphasis><Emphasis Type="Italic">join</Emphasis> queries in relational databases

Ranking queries, also known as top-k queries, produce results that are ordered on some computed score. Typically, these queries involve joins, where users are usually interested only in the top-k join results. Top-k queries are dominant in many emerging applications, e.g., multimedia retrieval by content, Web databases, data mining, middlewares, and most information retrieval applications. Current relational query processors do not handle ranking queries efficiently, especially when joins are involved. In this paper, we address supporting top-k join queries in relational query processors. We introduce a new rank-join algorithm that makes use of the individual orders of its inputs to produce join results ordered on a user-specified scoring function. The idea is to rank the join results progressively during the join operation. We introduce two physical query operators based on variants of ripple join that implement the rank-join algorithm. The operators are nonblocking and can be integrated into pipelined execution plans. We also propose an efficient heuristic designed to optimize a top-k join query by choosing the best join order. We address several practical issues and optimization heuristics to integrate the new join operators in practical query processors. We implement the new operators inside a prototype database engine based on PREDATOR. The experimental evaluation of our approach compares recent algorithms for joining ranked inputs and shows superior performance.Received: 23 December 2003, Accepted: 31 March 2004, Published online: 12 August 2004Edited by: S. AbiteboulExtended version of the paper published in the Proceedings of the 29th International Conference on Very Large Databases, VLDB 2003, Berlin, Germany, pp 754-765     

数据修复与一致性查询处理研究

在数据库以及集成系统中通常存在违背数据约束的不一致查询问题。修复是解决该问题的主要手段之一,但目前还缺乏基于修复、约束与查询的统一模型研究。提出了基于删除元组修复、满足多种类型约束的一致性查询算法;阐明了具有简洁特性的约束定义与查询语句结构;构建了新的查询与修复系统模型,将关系实例集、非空的约束集、查询定义、修复方法等统一到模型中,以产生满足一致性约束要求的查询结果。所研究的方法、语言以及模型通用性强、适用面广,不局限于特定质量问题的修复与查询。     

面向存在不确定对象的组最近邻查询方法

组最近邻查询是空间对象查询领域的一类重要查询,通过该查询可找到距离给定查询点集最近的空间对象.由于图像分辨率或解析度的限制等因素,空间对象的存在不确定性广泛存在于某些涉及图像处理的查询应用中.这些对象位置数据的存在不确定性会对组最近邻查询结果产生影响.本文给出面向存在不确定对象的概率阈值组最近邻查询定义,设计了高效的查询处理机制,通过剪枝优化等手段提高概率阈值组最近邻查询效率,并进一步提出了高效概率阈值组最近邻查询算法.采用多个真实数据集对概率阈值组最近邻算法进行了实验验证,结果表明所提算法具有良好的查询效率.     

Privacy-Conscious Location-Based Queries in Mobile Environments

In location-based services, users with location-aware mobile devices are able to make queries about their surroundings anywhere and at any time. While this ubiquitous computing paradigm brings great convenience for information access, it also raises concerns over potential intrusion into user location privacy. To protect location privacy, one typical approach is to cloak user locations into spatial regions based on user-specified privacy requirements, and to transform location-based queries into region-based queries. In this paper, we identify and address three new issues concerning this location cloaking approach. First, we study the representation of cloaking regions and show that a circular region generally leads to a small result size for region-based queries. Second, we develop a mobility-aware location cloaking technique to resist trace analysis attacks. Two cloaking algorithms, namely MaxAccu_Cloak and MinComm_Cloak, are designed based on different performance objectives. Finally, we develop an efficient polynomial algorithm for evaluating circular-region-based kNN queries. Two query processing modes, namely bulk and progressive, are presented to return query results either all at once or in an incremental manner. Experimental results show that our proposed mobility-aware cloaking algorithms significantly improve the quality of location cloaking in terms of an entropy measure without compromising much on query latency or communication cost. Moreover, the progressive query processing mode achieves a shorter response time than the bulk mode by parallelizing the query evaluation and result transmission.     

Efficient data dissemination using locale covers

Location-dependent data are central to many emerging applications, ranging from traffic information services to sensor networks. The standard pull- and push-based data dissemination models become unworkable since the data volumes and number of clients are high.We address this problem using locale covers, a subset of the original set of locations of interest, chosen to include at least one location in a suitably defined neighborhood of any client. Since location-dependent values are highly correlated with location, a query can be answered using a location close to the query point. Typical closeness measures might be Euclidean distance, or a $k$-nearest neighbor criterion.We show that location-dependent queries may be answered satisfactorily using locale covers. Our approach is independent of locations and speeds of clients, and is applicable to mobile clients.We also introduce a nested locale cover scheme that ensures fair access latencies, and allows clients to refine the accuracy of their information over time. We also prove two important results: one regarding the greedy algorithm for sensor covers and the other pertaining to randomized locale covers for $k$-nearest neighbor queries.     

一种障碍空间数据库中的连续反k近邻查询方法

随着智能移动设备和无线定位技术的飞速发展,使用基于位置服务应用的用户越来越多.特别地,不同于传统的针对固定位置的快照查询,移动的用户往往基于移动轨迹发出连续的查询.在真实和虚拟的空间环境中,障碍物的影响都是广泛存在的,障碍空间内的查询处理技术得到了越来越多的关注,其中,障碍空间内的连续反k近邻查询处理有着重要的应用.对障碍空间中的连续反k近邻查询问题进行了定义和系统的研究,通过定义控制点和分割点,提出了针对该问题的处理框架.进一步地,提出了一系列的过滤和求精算法,包括剪枝数据集、获取障碍物、剪枝和计算控制点和更新结果集等处理策略.基于多种数据集对所提出的算法进行了实验评估.与针对每个数据点进行k 近邻计算的基本方法相比,这些方法可以大幅度提高查询处理的CPU 和I/O 效率.     

REPS:一种高效的容错并行概率流Skyline查询方法

概率数据流的并行Skyline查询作为当前大数据分析的一个重要方面,在诸多实际应用中发挥着重要作用。针对并行概率流Skyline查询过程中因发生故障而导致查询结果不准确和查询中断等问题,提出了一种基于复制的容错并行Skyline查询方法REPS。该方法选择参与并行处理的计算节点作为副本节点,并采用层次-循环式数据副本放置策略,选择优先级高的副本恢复数据来保证数据恢复的高效性;同时将故障检测、丢失数据恢复和查询过程恢复贯穿于整个查询更新过程中,以减少容错处理的额外通信和计算开销,并实现快速的容错并行查询。实验结果表明,REPS方法不仅在无故障发生和单个节点失效时具有较高的查询处理效率,而且对于多节点失效情形,仍然能够保持较高的查询处理速率且满足查询需求。     

Aggregate nearest neighbor queries in road networks

Aggregate nearest neighbor queries return the object that minimizes an aggregate distance function with respect to a set of query points. Consider, for example, several users at specific locations (query points) that want to find the restaurant (data point), which leads to the minimum sum of distances that they have to travel in order to meet. We study the processing of such queries for the case where the position and accessibility of spatial objects are constrained by spatial (e.g., road) networks. We consider alternative aggregate functions and techniques that utilize Euclidean distance bounds, spatial access methods, and/or network distance materialization structures. Our algorithms are experimentally evaluated with synthetic and real data. The results show that their relative performance depends on the problem characteristics.     

Answering why-not questions on KNN queries

Being decades of study, the usability of database systems have received more attention in recent years. Now it is especially able to explain missing objects in a query result, which is called “why-not” questions, and is the focus of concern. This paper studies the problem of answering whynot questions on KNN queries. In our real life, many users would like to use KNN queries to investigate the surrounding circumstances. Nevertheless, they often feel disappointed when finding the result not including their expected objects. In this paper, we use the query refinement approach to resolve the problem. Given the original KNN query and a set of missing objects as input, our algorithm offer a refined KNN query that includes the missing objects to the user. The experimental results demonstrate the efficiency of our proposed optimizations and algorithms.