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.     

动态权重路网下的连续近邻查询

现有的近邻查询在查询相同或相近目标时,会得到相同的行驶路线,从而导致大量用户聚集到该区域,造成二次拥堵。针对上述问题,提出一种支配关系监控算法。该算法采用实时交通信息作为动态权重,并给出一个在路网权重变化下的连续k近邻查询方法,有效地避免二次拥堵。实验结果验证了该算法的有效性和高效性。     

Efficient Evaluation of Probabilistic Advanced Spatial Queries on Existentially Uncertain Data

We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries, nearest neighbors, spatial skylines, and reverse nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.     

The partial sequenced route query with traveling rules in road networks

In modern geographic information systems, route search represents an important class of queries. In route search related applications, users may want to define a number of traveling rules (traveling preferences) when they plan their trips. However, these traveling rules are not considered in most existing techniques. In this paper, we propose a novel spatial query type, the multi-rule partial sequenced route (MRPSR) query, which enables efficient trip planning with user defined traveling rules. The MRPSR query provides a unified framework that subsumes the well-known trip planning query (TPQ) and the optimal sequenced route (OSR) query. The difficulty in answering MRPSR queries lies in how to integrate multiple choices of points-of-interest (POI) with traveling rules when searching for satisfying routes. We prove that MRPSR query is NP-hard and then provide three algorithms by mapping traveling rules to an activity on vertex network. Afterwards, we extend all the proposed algorithms to road networks. By utilizing both real and synthetic POI datasets, we investigate the performance of our algorithms. The results of extensive simulations show that our algorithms are able to answer MRPSR queries effectively and efficiently with underlying road networks. Compared to the Light Optimal Route Discoverer (LORD) based brute-force solution, the response time of our algorithms is significantly reduced while the distances of the computed routes are only slightly longer than the shortest route.     

Aggregate location recommendation in dynamic transportation networks

Travel planning and location recommendation are increasingly important in recent years. In this light, we propose and study a novel aggregate location recommendation query (ALRQ) of discovering aggregate locations for multiple travelers and planning the corresponding travel routes in dynamic transportation networks. Assuming the scenario that multiple travelers target the same destination, given a set of travelers’ locations Q, a set of potential aggregate location O, and a departure time t, the ALRQ finds an aggregate location o ∈ O that has the minimum global travel time \({\sum }_{q \in Q} T(q,o,t)\), where T(q,o,t) is the travel time between o and q with departure time t. The ALRQ problem is challenging due to three reasons: (1) how to model the dynamic transportation networks practically, and (2) how to compute ALRQ efficiently. We take two types of dynamic transportation networks into account, and we define a pair of upper and lower bounds to prune the search space effectively. Moreover, a heuristic scheduling strategy is adopted to schedule multiple query sources. Finally, we conducted extensive experiments on real and synthetic spatial data to verify the performance of the developed algorithms.     

SMashQ: spatial mashup framework for k-NN queries in time-dependent road networks

The k-nearest-neighbor (k-NN) query is one of the most popular spatial query types for location-based services (LBS). In this paper, we focus on k-NN queries in time-dependent road networks, where the travel time between two locations may vary significantly at different time of the day. In practice, it is costly for a LBS provider to collect real-time traffic data from vehicles or roadside sensors to compute the best route from a user to a spatial object of interest in terms of the travel time. Thus, we design SMashQ, a server-side spatial mashup framework that enables a database server to efficiently evaluate k-NN queries using the route information and travel time accessed from an external Web mapping service, e.g., Microsoft Bing Maps. Due to the expensive cost and limitations of retrieving such external information, we propose three shared execution optimizations for SMashQ, namely, object grouping, direction sharing, and user grouping, to reduce the number of external Web mapping requests and provide highly accurate query answers. We evaluate SMashQ using Microsoft Bing Maps, a real road network, real data sets, and a synthetic data set. Experimental results show that SMashQ is efficient and capable of producing highly accurate query answers.     

室内概率阈值反向最近邻查询

室内空间变得越发的庞大和复杂,随之产生了越来越多的室内空间查询需求.目前已有文献提出了针对室内空间环境的范围查询和最近邻查询,而作为常见的空间查询类型的反向最近邻查询,尚未有相关的研究.为此,提出了室内概率阈值反向最近邻查询和基于定位设备的设备可达图模型.在图模型基础上,提出了室内概率阈值反向最近邻查询处理算法,该算法由基于图模型的批量剪枝、基于室内距离的剪枝、基于概率的剪枝和概率计算4部分构成,通过剪枝策略修剪掉不可能出现在结果集中的对象,从而缩小了查询空间,提高了效率.     

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.     

Dynamic Shortest Path Monitoring in Spatial Networks

With the increasing availability of real-time traffic information, dynamic spatial networks are pervasive nowadays and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks (DSPM query). When a traveler aims to a destination, his/her shortest path to the destination may change due to two reasons: 1) the travel costs of some edges have been updated and 2) the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons: 1) how to maintain and reuse the existing computation results to accelerate the following computations, and 2) how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data.     

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.     

TARS: traffic-aware route search

In a traffic-aware route search (TARS), the user provides start and target locations and sets of search terms. The goal is to find the fastest route from the start location to the target via geographic entities (points of interest) that correspond to the search terms, while taking into account variations in the travel speed due to changes in traffic conditions, and the possibility that some visited entities will not satisfy the search requirements. A TARS query may include temporal constraints and order constraints that restrict the order by which entities are visited. Since TARS generalizes the Traveling-Salesperson Problem, it is an NP-hard problem. Thus, it is unlikely to find a polynomial-time algorithm for evaluating TARS queries. Hence, we present in this paper three heuristics to answer TARS queries—a local greedy approach, a global greedy approach and an algorithm that computes a linear approximation to the travel speeds, formulates the problem as a Mixed Integer Linear Programming (MILP) problem and uses a solver to find a solution. We provide an experimental evaluation based on actual traffic data and show that using a MILP solver to find a solution is effective and can be done within a limited running time in many real-life scenarios. The local-greedy approach is the least effective in finding a fast route, however, it has the best running time and it is the most scalable.     

A generalized target-driven cache replacement policy for mobile environments

Caching frequently accessed data items on the client side is an effective technique to improve the system performance in wireless networks. Due to cache size limitations, cache replacement algorithms are used to find a suitable subset of items for eviction from the cache. Many existing cache replacement algorithms employ a value function of different factors such as time since last access, entry time of the item in the cache, transfer time, item expiration time and so on. However, most of the existing algorithms are designed for WWW environment under weak consistency model. Their choices of value functions are based on experience and on a value function which only works for a specific performance metric.In this paper, we propose a generalized value function for cache replacement algorithms for wireless networks under a strong consistency model. The distinctive feature of our value function is that it is generalized and can be used for various performance metrics by making the necessary changes. Further, we prove that the proposed value function can optimize the access cost in our system model. To demonstrate the practical effectiveness of the generalized value function, we derive two specific functions and evaluate them by setting up two different targets: minimizing the query delay and minimizing the downlink traffic. Compared to previous schemes, our algorithm significantly improves the performance in terms of query delay or in terms of bandwidth utilization depending on the specified target.     

Robust network planning in nonuniform traffic scenarios

Robustness to the environmental variations is an important feature of any reliable communication network. This paper reports on a network theory approach to the design of such networks where the environmental changes are traffic fluctuations, topology modifications, and changes in the source of external traffic. Motivated by the definition of betweenness centrality in network science, we introduce the notion of traffic-aware betweenness (TAB) for data networks, where usually an explicit (or implicit) traffic matrix governs the distribution of external traffic into the network. We use the average normalized traffic-aware betweenness, which is referred to as traffic-aware network criticality (TANC), as our main metric to quantify the robustness of a network. We show that TANC is directly related to some important network performance metrics, such as average network utilization and average network cost. We prove that TANC is a linear function of end-to-end effective resistances of the graph. As a result, TANC is a convex function of link weights and can be minimized using convex optimization techniques. We use semi-definite programming method to study the properties of the optimization problem and derive useful results to be employed for robust network planning purposes.     

动态网格下的资源发现算法*

提出了一种网格环境下动态资源的表示方法——矩阵表示法,同时研究了矩阵表示法下的资源查找和更新算法,该算法充分考虑了资源属性的动态性。由于矩阵计算不用操作资源的原始数据,从而提高了查找的效率,不仅能够进行精确匹配的查询也能进行范围查询。在路由查询时,只要参考本地信息就可给出准确的路由选择。矩阵表示资源还简化了动态资源的更新过程,使资源信息能够及时接近真实的网格环境。     

DART+: Direction-aware bichromatic reverse k nearest neighbor query processing in spatial databases

This article presents a novel type of queries in spatial databases, called the direction-aware bichromatic reverse k nearest neighbor(DBRkNN) queries, which extend the bichromatic reverse nearest neighbor queries. Given two disjoint sets, P and S, of spatial objects, and a query object q in S, the DBRkNN query returns a subset P′ of P such that k nearest neighbors of each object in P′ include q and each object in P′ has a direction toward q within a pre-defined distance. We formally define the DBRkNN query, and then propose an efficient algorithm, called DART, for processing the DBRkNN query. Our method utilizes a grid-based index to cluster the spatial objects, and the B⁺-tree to index the direction angle. We adopt a filter-refinement framework that is widely used in many algorithms for reverse nearest neighbor queries. In the filtering step, DART eliminates all the objects that are away from the query object more than a pre-defined distance, or have an invalid direction angle. In the refinement step, remaining objects are verified whether the query object is actually one of the k nearest neighbors of them. As a major extension of DART, we also present an improved algorithm, called DART+, for DBRkNN queries. From extensive experiments with several datasets, we show that DART outperforms an R-tree-based naive algorithm in both indexing time and query processing time. In addition, our extension algorithm, DART+, also shows significantly better performance than DART.     

Bandwidth-constrained queries in sensor networks

Sensor networks consist of battery-powered wireless devices that are required to operate unattended for long periods of time. Thus, reducing energy drain is of utmost importance when designing algorithms and applications for such networks. Aggregate queries are often used by monitoring applications to assess the status of the network and detect abnormal behavior. Since radio transmission often constitutes the biggest factor of energy drain in a node, in this paper we propose novel algorithms for the evaluation of bandwidth- constrained queries over sensor networks. The goal of our techniques is, given a target bandwidth utilization factor, to program the sensor nodes in a way that seeks to maximize the accuracy of the produced query results at the monitoring node, while always providing strong error guarantees to the monitoring application. This is a distinct difference of our framework from previous techniques that only provide probabilistic guarantees on the accuracy of the query result. Our algorithms are equally applicable when the nodes have ample power resources, but bandwidth consumption needs to be minimized, for instance in densely distributed networks, to ensure proper operation of the nodes. Our experiments with real sensor data show that bandwidth-constrained queries can substantially reduce the number of messages in the network while providing very tight error bounds on the query result.     

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.     

Instance optimal query processing in spatial networks

The performance optimization of query processing in spatial networks focuses on minimizing network data accesses and the cost of network distance calculations. This paper proposes algorithms for network k-NN queries, range queries, closest-pair queries and multi-source skyline queries based on a novel processing framework, namely, incremental lower bound constraint. By giving high processing priority to the query associated data points and utilizing the incremental nature of the lower bound, the performance of our algorithms is better optimized in contrast to the corresponding algorithms based on known framework incremental Euclidean restriction and incremental network expansion. More importantly, the proposed algorithms are proven to be instance optimal among classes of algorithms. Through experiments on real road network datasets, the superiority of the proposed algorithms is demonstrated.     

Probabilistic cost model for nearest neighbor search in image retrieval

We present a probabilistic cost model to analyze the performance of the kd-tree for nearest neighbor search in the context of content-based image retrieval. Our cost model measures the expected number of kd-tree nodes traversed during the search query. We show that our cost model has high correlations with both the observed number of traversed nodes and the runtime performance of search queries used in image retrieval. Furthermore, we prove that, if the query points follow the distribution of data used to construct the kd-trees, the median-based partitioning method as well as PCA-based partitioning technique can produce near-optimal kd-trees in terms of minimizing our cost model. The probabilistic cost model is validated through experiments in SIFT-based image retrieval.     

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.