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.     

Efficient trip scheduling algorithms for groups

Given the source and destination locations of $n$ group members and a set of required point of interest (POI) types such as restaurants and shopping centers, a Group Trip Scheduling (GTS) query schedules $n$ individual trips such that each POI type is included in exactly one trip and an aggregate trip overhead distance for visiting the required POI types is minimized. Each trip starts at a member’s source location, goes through some POIs, and ends at the member’s destination location. The trip distance of a group member is the distance from her source to destination via the POIs that the group member visits, and the trip overhead distance of the group member is measured by subtracting the distance between her source and destination locations (without visiting any POI type) from her trip distance. The aggregate trip overhead distance is either the summation or the maximum of the trip overhead distances of the group members for visiting the POIs. A GTS query enables a group to schedule independent trips for its members in order to perform a set of tasks with the minimum travel cost. For example, family members normally have many outdoor tasks to perform within a short time for the proper management of home. The members may need to go to a bank to withdraw or deposit money, a pharmacy to buy medicine, or a supermarket to buy groceries. Similarly, organizers of an event may need to visit different POI types to perform many tasks. These scenarios motivate us to introduce a GTS query, a novel query type in spatial databases. We develop an efficient approach to process GTS queries and variants for the Euclidean space and road networks. By exploiting geometric properties, we refine the POI search space and prune POIs, which in turn reduce the query processing overhead significantly. In addition, we propose a dynamic programming technique to eliminate the trip combinations that cannot be part of the optimal query answer. We show that processing a GTS query is NP-hard and propose an approximation algorithm to further reduce the query processing overhead. We perform extensive experiments using real and synthetic datasets and show that our approach outperforms a straightforward approach with a large margin.     

大规模路网图下关键词覆盖最优路径查询优化

游客倾向于采用个性化的旅游路线,规划这样的路线需要综合考量路径长度、路径开销和路径覆盖的兴趣点.关键词覆盖最优路径查询（KOR）就是用于规划这样的路线的一类查询,其处理过程通常包括预处理和路径拓展.由于路网图规模的不断扩大,现有算法预处理所需内存开销急剧上升,由于内存不足,导致较大规模的路网不能处理;路径拓展搜索空间快速膨胀,应用场景可扩展性与查询实时性难以保证.针对这些问题,提出一种大规模路网图下关键词覆盖最优路径查询算法KORL.KORL在预处理阶段将路网划分为若干子图,仅保存子图内路径和子图之间路径的信息,以减小预处理所需内存.在路径拓展阶段,综合运用最小代价剪枝、近似支配剪枝、全局优先拓展和关键词顶点拓展等策略对现有算法进行优化,以高效地搜索近似最优解.采用美国各地区的路网图,在16G内存环境下进行实验,突破了现有算法只能处理顶点数不超过25K路网图的限制.实验结果表明,KORL算法具有良好的可扩展性.     

Discovery of probabilistic nearest neighbors in traffic-aware spatial networks

Travel planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of discovering probabilistic nearest neighbors and planning the corresponding travel routes in traffic-aware spatial networks (TANN queries) to avoid potential time delay/traffic congestions. We propose and study four novel probabilistic TANN queries. Thereinto two queries target at minimizing the travel time, including a congestion-probability threshold query, and a time-delay threshold query, while another two travel-time threshold queries target at minimizing the potential time delay/traffic congestion. We believe that TANN queries are useful in many real applications, such as discovering nearby points of interest and planning convenient travel routes for users, and location based services in general. The TANN queries are challenged by two difficulties: (1) how to define probabilistic metrics for nearest neighbor queries in traffic-aware spatial networks, and (2) how to process these TANN queries efficiently under different query settings. To overcome these challenges, we define a series of new probabilistic metrics and develop four efficient algorithms to compute the TANN queries. The performances of TANN queries are verified by extensive experiments on real and synthetic spatial data.     

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).     

Continuous range-based skyline queries in road networks

With the ever-growing popularity of smartphone devices in recent years, skyline queries over spatial Web objects in road networks have received increasing attention. In the literature, various techniques have been developed to tackle skyline queries that take both spatial and non-spatial attributes into consideration. However, the existing solutions only focus on solving point-based queries, where the query location is a spatial point. We observe that in many real-life applications, the user location is often represented by a spatial range. Thus, in this paper, we study a new problem of range-based skyline queries (CRSQs) in road networks. Two efficient algorithms named landmark-based (LBA) and index-based (IBA) algorithms are proposed. We also present incremental versions of LBA and IBA to handle continuous range-based skyline queries over moving objects. Extensive experiments using real road network datasets demonstrate the effectiveness and efficiency of our proposed algorithms.     

Optimal group route query: Finding itinerary for group of users in spatial databases

The increasing popularity of location-based applications creates new opportunities for users to travel together. In this paper, we study a novel spatio-social optimization problem , i.e., Optimal Group Route, for multi-user itinerary planning. With our problem formulation, users can individually specify sources and destinations, preferences on the Point-of-interest (POI) categories, as well as the distance constraints. The goal is to find a itinerary that can be traversed by all the users while maximizing the group’s preference of POI categories in the itinerary. Our work advances existing group trip planning studies by maximizing the group’s social experience. To this end, individual preferences of POI categories are aggregated by considering the agreement and disagreement among group members. Furthermore, planning a multi-user itinerary on large road networks is computationally challenging. We propose two efficient greedy algorithms with bounded approximation ratio, one exact solution which computes the optimal itinerary by exploring a limited number of paths in the road network, and a scaled approximation algorithm to speed up the dynamic programming employed by the exact solution. We conduct extensive empirical evaluations on two real-world road network/POI datasets and our results confirm the effectiveness and efficiency of our solutions.     

Popular route planning with travel cost estimation from trajectories

With the increasing number of GPS-equipped vehicles,more and more trajectories are generated continuously,based on which some urban applications become feasible,such as route planning.In general,popular route that has been travelled frequently is a good choice,especially for people who are not familiar with the road networks.Moreover,accurate estimation of the travel cost(such as travel time,travel fee and fuel consumption)will benefit a wellscheduled trip plan.In this paper,we address this issue by finding the popular route with travel cost estimation.To this end,we design a system consists of three main components.First,we propose a novel structure,called popular traverse graph where each node is a popular location and each edge is a popular route between locations,to summarize historical trajectories without road network information.Second,we propose a self-adaptive method to model the travel cost on each popular route at different time interval,so that each time interval has a stable travel cost.Finally,based on the graph,given a query consists of source,destination and leaving time,we devise an efficient route planning algorithmwhich considers optimal route concatenation to search the popular route from source to destination at the leaving time with accurate travel cost estimation.Moreover,we conduct comprehensive experiments and implement our system by a mobile App,the results show that our method is both effective and efficient.     

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。     

Metro Transit‐Centric Visualization for City Tour Planning

In general, city trip planning consists of two main steps: knowing Points‐Of‐Interest (POIs), and then planning a tour route from the current point to next preferred POIs. We mainly consider the metro for traveling around touristic cities as the main means of transportation. In this context, existing tools lack a capability to effectively visualize POIs on the metro map for trip planning. To bridge this gap, we propose an interactive framework that holistically combines presentations of POIs and a metro network. Our idea is to identify popular POIs based on visual worth computation, and to introduce POI discovery for effectively identifying POIs within reach of a metro network for users. We use octilinear layouts to highlight the metro network, and show representative POI images in the layout space visualized within a user‐specified viewing window. We have implemented our working prototype showing touristic cities with a metro network. We have factored out various design guidelines that are basis for designing our method, and validated our approach with a user study surveying 70 individuals.     

路网中移动对象快照K近邻查询处理

扩展了一种支持路网中移动对象的位置相关查询框架的功能,利用存在磁盘上的R树来存储网络连通性和一种基于内存的网格结构来维持移动对象的位置更新,提出了基于范围查询(MNDR)的快照K近邻查询算法(SKNN),对空间中的任意一条边,分析可能受影响的最大数量和最小数量的网格单元格,说明用于快照范围查询处理的搜索空间的最大范围,预估包含查询结果的子空间,使用这个子空间作为范围调用MNDR来有效地计算路网中查询点的KNN POI,降低I/O成本,缩短查询时间。通过实验对比,当规模扩展到数十万的移动对象时,SKNN比种有效查询处理空间网络数据的预计算方法S-GRID有更好大的系统吞吐量。     

On personalized and sequenced route planning

Online trip planning is a popular service that has facilitated a lot of people greatly. However, little attention has been paid to personalized trip planning which is even more useful. In this paper, we define a highly expressive personalized route planning query-the Personalized and Sequenced Route (PSR) Query which considers both personalization and sequenced constraint, and propose a novel framework to deal with the query. The framework consists of three phases: guessing, crossover and refinement. The guessing phase strives to obtain one high quality route as the baseline to bound the search space into a circular region. The crossover phase heuristically improve the quality of multiple guessed routes via a modified genetic algorithm, which further narrows the radius of the search space. The refinement phase backwardly examines each candidate point and partial route to rule out impossible ones. The combination of these phases can efficiently and effectively narrow our search space via a few iterations. In the experiment part, we firstly show our evaluation results of each phase separately, proving the effectiveness of each phase. Then, we present the evaluation results of the combination of them, which offers insight into the merits of the proposed framework.     

贪心与回溯算法在城市马拉松路线规划中的实践

针对目前城市马拉松路线人工规划效率低下的问题, 本文采用贪心和回溯算法进行城市马拉松路线智能规划, 具体方法是: 通过城市路网信息构建由经纬度坐标点拓扑关系连接而成的路网, 采用贪心和回溯算法对坐标点进行遍历搜索, 结合城市马拉松路线特殊要求, 运用直接逼近、启发式远离、启发式靠近和方向预估等策略实现路线的智能规划. 在此基础上, 提出一种综合POI热度值、道路宽度适宜度、路线畅通指数、过弯舒适度以及POI密集度5个维度的马拉松路线评估方法. 最后, 开展了北京、合肥马拉松人工和智能规划路线对比分析, 结果表明所采用的方法可快速高效实现马拉松路线规划.     

路网中双色数据集上连续反向k近邻查询处理的研究

近年来,反向最近邻查询(RNN)算法研究得到了普遍的关注,成为了数据库领域的一个研究热点。欧氏空 间中提出了较多的高效算法,而路网中的反向最近邻处理方面所做的工作不够,有关这方面的成果较少。路网中查询 点和数据对象之间以及不同数据对象之间的距离受到路网连通性的影响,欧氏空间中的反向最近部方法在路网中不 适用。反向最近部查询有两种类型:单色反向最近部查询(Monochromatic RNN, MRNN)和双色反向最近部查询(13i- chromatic RNN,13RNN)。到目前为止,仍然没有有效的算法来处理路网中双色数据集上的连续反向k近部查询。因 此,研究路网中双色数据集上连续反向k近部查询是很有意义的。     

Efficient Metric All-k-Nearest-Neighbor Search on Datasets Without Any Index

An all-k-nearest-neighbor (AkNN) query finds k nearest neighbors for each query object. This problem arises naturally in many areas, such as GIS (geographic information system), multimedia retrieval, and recommender systems. To support various data types and flexible distance metrics involved in real applications, we study AkNN retrieval in metric spaces, namely, metric AkNN (MAkNN) search. Consider that the underlying indexes on the query set and the object set may not exist, which is natural in many scenarios. For example, the query set and the object set could be the results of other queries, and thus, the underlying indexes cannot be built in advance. To support MAkNN search on datasets without any underlying index, we propose an efficient disk-based algorithm, termed as Partition-Based MAkNN Algorithm (PMA), which follows a partition-search framework and employs a series of pruning rules for accelerating the search. In addition, we extend our techniques to tackle an interesting variant of MAkNN queries, i.e., metric self-AkNN (MSAkNN) search, where the query set is identical to the object set. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness of our pruning rules and the efficiency of the proposed algorithms, compared with state-of-the-art MAkNN and MSAkNN algorithms.     

Efficient Path Routing Over Road Networks in the Presence of Ad-Hoc Obstacles

Nowadays, the path routing over road networks has become increasingly important, yet challenging, in many real-world applications such as location-based services (LBS), logistics and supply chain management, transportation systems, map utilities, and so on. While many prior works aimed to find a path between a source and a destination with the smallest traveling distance/time, they do not take into account the quality constraints (e.g., obstacles) of the returned paths, such as uneven roads, roads under construction, and weather conditions on roads. Inspired by this, in this paper, we consider two types of ad-hoc obstacles, keyword-based and weather-based obstacles, on road networks, which can be used for modeling roads that the returned paths should not pass through. In the presence of such ad-hoc obstacles on roads, we formulate a path routing query over road networks with ad-hoc obstacles (PRAO), which retrieves paths from source to destination on road networks that do not pass ad-hoc keyword and weather obstacles and have the smallest traveling time. In order to efficiently answer PRAO queries, we design effective pruning methods and indexing mechanism to facilitate efficient PRAO query answering. Extensive experiments have demonstrated the efficiency and effectiveness of our approaches over real/synthetic data sets.     

Search on transportation networks for location-based service

The issue of how to provide location-based service (LBS) attracted many researchers. In this paper, we focus on a typical situation of LBS which is to provide services for users in cars that move in a road network. To provide such kind of services, an integration method for representing transportation information with a road map is proposed. By using our integration method, since the transportation information of road networks is managed under the spatial index structure created for road networks, spatial queries on them can take advantages of the spatial index structure and achieve an efficient process. Moreover, we discuss path search, region search, nearest neighbor search and continuous nearest neighbor search in this paper, which are based on transportation networks with (or without) considering the static spatial objects outside the transportation networks. Using transportation information and the corresponding real road network, the paper offers evaluations by comparing our representation method and query method with those in related works. The results show a good performance of our methods.     

Continuous reverse k nearest neighbor monitoring on moving objects in road networks

Continuous reverse k nearest neighbor (CRkNN) monitoring in road networks has recently received increasing attentions. However, there is still a lack of efficient CRkNN algorithms in road networks up to now. In road networks, moving query objects and data objects are restricted by the connectivity of the road network and both the object–query distance and object–object distance updates affect the result of CRkNN queries. In this paper, we present a novel algorithm for continuous and incremental evaluation of CRkNN queries in road networks. Our method is based on a novel data structure called dual layer multiway tree (DLM tree) we proposed to represent the whole monitoring region of a CRkNN query q. We propose several lemmas to reduce the monitoring region of q and the number of candidate objects as much as possible. Moreover, by associating a variable NN_count with each candidate object, we can simplify the monitoring of candidate objects. There are a large number of objects roaming in a road network and many of them are irrelevant to a specific CRkNN query of a query object q. To minimize the processing extension, for a road in the network, we give an IQL list and an IQCL list to specify the set of query objects and data objects whose location updates should be maintained for CRkNN processing of query objects. Our CRkNN method consists of two phase: the initial result generating phase and incremental maintenance phase. In each phase, algorithms with high performance are proposed to make our CRkNN method more efficient. Extensive simulation experiments are conducted and the result shows that our proposed approach is efficient and scalable in processing CRkNN queries in road networks.     

路网环境中关于模糊组最近邻问题的研究

为了解决路网环境中传统的组最近邻查询无法支持用户不确定搜索的问题,在组最近邻查询的基础上引入了“模糊”因子来描述用户查询的不确定性,并提出了四种不同的算法,其中朴素的全局搜索算法利用了Dijkstra 算法的特性来处理不确定性,多维向量算法和V-Tree 算法在此基础上通过缩小搜索空间进一步优化,最后提出的近似算法在牺牲了一定正确率的前提下进一步提高了查询效率。通过在真实路网数据集上的大量实验,总结归纳了不同算法的优势,并充分验证了各个算法的合理性与实用性。     

Searching for people to follow in social networks

With the development of social networks, more and more users have a great need to search for people to follow (SPTF) to receive their tweets. According to our experiments, approximately 50% of social networks’ lost users leave due to a lack of people to follow. In this paper, we define the problem of SPTF and propose an approach to give users tags and then deliver a ranked list of valuable accounts for them to follow. In the proposed approach, we first seek accounts related to keywords via expanding and predicting tags for users. Second, we propose two algorithms to rank relevant accounts: the first mines the forwarded relationship, and the second incorporates the following relationship into PageRank. Accordingly, we have built a search system¹ that to date, has received more than 1.7 million queries from 0.2 million users. To evaluate the proposed approach, we created a crowd-sourcing organization and crawled 0.25 billion profiles, 15 billion messages and 20 billion links representing following relationships on Sina Microblog. The empirical study validates the effectiveness of our algorithms for expanding and predicting tags compared to the baseline. From query logs, we discover that hot queries include keywords related to academics, occupations and companies. Experiments on those queries show that PageRank-like algorithms perform best for occupation-related queries, forward-relationship-like algorithms work best for academic-related queries and domain-related headcount algorithms work best for company-related queries.