Continuous range-based skyline queries in road networks

The range-based skyline (r-skyline) query on road networks retrieves the skyline objects of taking each point within a road region as a query point, in terms of objects' spatial and non-spatial attributes. In this paper, we systematically carry out the study of why-not questions on the r-skyline query in the road network (abbreviated as the why-not RSQ problem). We present three modification strategies, including modifying the query range, modifying the why-not point, and modifying both of them, for the why-not RSQ problem. In particular, a suite of newly presented effective concepts/techniques are leveraged, such as the concepts of skyline scope and skyline dominance region, non-spatial attribute modification pruning, and G-tree index. Extensive experiments using both real and synthetic data sets demonstrate the performance of our proposed algorithms.

Path queries such as finding the shortest path in travel time from my hotel to the are heavily used in many applications of road networks. Currently, simple statistic aggregates such as the average travel time between two vertices are often used to answer path queries. However, such simple aggregates often cannot capture the uncertainty inherent in traffic. In this paper, we study how to take traffic uncertainty into account in answering path queries in road networks. To capture the uncertainty in traffic such as the travel time between two vertices, the weight of an edge is modeled as a random variable and is approximated by a set of samples. We propose three novel types of probabilistic path queries using basic probability principles: (1) a probabilistic path query like what are the paths from my hotel to the airport whose travel time is at most 30 minutes with a probability of at least 90%?; (2) a weight-threshold top-k path query like what are the top-3 paths from my hotel to the airport with the highest probabilities to take at most 30 minutes?; and (3) a probability-threshold top-k path query like what are the top-3 shortest paths from my hotel to the airport whose travel time is guaranteed by a probability of at least 90%? To evaluate probabilistic path queries efficiently, we develop three efficient probability calculation methods: an exact algorithm, a constant factor approximation method and a sampling based approach. Moreover, we devise the P* algorithm, a best-first search method based on a novel hierarchical partition tree index and three effective heuristic evaluation functions. An extensive empirical study using real road networks and synthetic data sets shows the effectiveness of the proposed path queries and the efficiency of the query evaluation methods.

A k-Range Nearest Neighbor (or kRNN for short) query in road networks finds the k nearest neighbors of every point on the road segments within a given query region based on the network distance. The kRNN query is significantly important for location-based applications in many realistic scenarios. For example, (1) the user's location is uncertain, i.e., user's location is modeled by a spatial region, and (2) the user is not willing to reveal her exact location to preserve her privacy, i.e., her location is blurred into a spatial region. However, the existing solutions for kRNN queries simply apply the traditional k-nearest neighbor query processing algorithm multiple times, which poses a huge redundant searching overhead. To this end, we propose an efficient kRNN query processing algorithm in this paper. Our algorithm (1) employs a shared execution approach to eliminate the redundant searching overhead, and (2) provides a parameter that can be tuned to achieve a tradeoff between the query processing performance and the storage overhead, while guaranteeing the user's exact k-nearest neighbors are included in the query answers. The experimental results show that our algorithm always outperforms the existing solution in terms of query response time, and the introduced tuning parameter is an effective way to achieve the tradeoff between the query response time and the storage overhead.

The range query of moving objects in a road network is widely used in battlefield environment, intelligent transportation systems, and mobile phone location. It has gradually become a research hotspot in the field of spatiotemporal data management. Most of the existing research on range query of moving objects in the road network is based on the premise of dense sampling of the device and complete storage of data. It ignores the uncertainty of discrete location data caused by a blind area of actual devices and limited memory, which cannot guarantee the accuracy of the range query. In this paper, the processing framework of probabilistic range query of moving objects in the road network is constructed. A spatiotemporal index structure is proposed based on the framework. This index can effectively represent time weights and the relationship between road sections. The probabilistic range query algorithm of moving objects in the road network with an uncertain trajectory caused by sampling frequency is designed and implemented. The experiments verify that the method proposed in this paper can improve query efficiency and ensure query accuracy.

Network Voronoi Diagram on uncertain objects for nearest neighbor queries

Skyline query processing for location-based services, which considers both spatial and nonspatial attributes of the objects being queried, has recently received increasing attention. Existing solutions focus on solving point- or line-based skyline queries, in which the query location is an exact location point or a line segment. However, due to privacy concerns and limited precision of localization devices, the input of a user location is often a spatial range. This paper studies a new problem of how to process such range-based skyline queries. Two novel algorithms are proposed: one is index-based (I-SKY) and the other is not based on any index (N-SKY). To handle frequent movements of the objects being queried, we also propose incremental versions of I-SKY and N-SKY, which avoid recomputing the query index and results from scratch. Additionally, we develop efficient solutions for probabilistic and continuous range-based skyline queries. Experimental results show that our proposed algorithms well outperform the baseline algorithm that adopts the existing line-based skyline solution. Moreover, the incremental versions of I-SKY and N-SKY save substantial computation cost, especially when the objects move frequently.

Aggregate nearest neighbor (ANN) query returns a common interesting data object that minimizes an aggregate distance for multiple query points. In this paper, we propose a novel approach to efficiently process ANN queries in road networks. This approach includes two processes: initializing process and pruning process, which are both based on computing kNN of query points in network Voronoi diagram. Experimental results show that our approach outperforms existing approaches on both response time and page accesses.

Location Based Services (LBS) are becoming very popular with today’s users. Some of these LBS require users to continuously send requests for services. This lead to leakages of both location and query contents to malicious adversaries. Further, if users are constrained by the nature of the road networks, an adversary can follow their path trajectory with ease. Most of the current privacy preserving solutions focus on temporal and spatial cloaking based methods to protect users’ location privacy. However, these solutions are vulnerable when subjected to continuous query environments. In this paper, we propose an optimized solution that preserves privacy for users’ trajectory for continuous LBS queries in road networks. First, we deploy a trusted third party architecture to provide anonymity for users as they use LBS services. Second, we utilize mix zone techniques and design two algorithms. The first algorithm, Abstraction Graph (AG), selects a sample of mix zones that satisfy the user desired privacy level under the acceptable service availability condition. The second algorithm, Optimized Decision Graph (ODG), utilizes the generated graph to find an optimal solution for the placement of mix zones through decomposition, chunking and replacement strategies. Finally, we analyze the capability of our algorithms to withstand attacks prone to mix zones and carry out experiments to verify this. The experiments results show that our Algorithms preserve privacy for users based on their privacy and service availability conditions.

The prediction of spatio-temporal data streams which is based on aggregate queries has been an important research direction in the research field of databases. More and more methods have been proposed to obtain approximate aggregate results. However, they will consume a lot of time and storage space. This paper proposes Dynamic Sketch DS index by using modified method of Adaptive Multi-dimensional Histogram AMH* to intelligently partition static sketch which can improve the approximate quality of aggregate queries in road networks. Then, based on DS index, this paper proposes a new prediction approach over data streams in road networks using Self-Adaptive Exponential Smoothing SAES.

Shortest path queries are ubiquitous in many spatial applications. Existing solutions assign numerical weights to edges and compute the path with the minimum sum of edge weights. However, in practice, the road categories associated with edges (e.g., toll) can make shortest paths undesirable, e.g., they may use unfavorable toll roads. Augmenting each edge with a label to denote its category, we study theLabel-Constrained Shortest Path(LCSP) query that finds the shortest path under the constraint that the edge labels along the path should follow a pattern expressed by a formal language. There have been extensive LCSP solutions, but they are either inefficient in query processing or limited to special languages with low expressiveness capacity. In this paper, we propose the index calledPartially Constrained Shortest Path(PCSP), which answers each query quickly by concatenating two shortest paths that partially satisfy the constraint and support more general regular languages. We also present pruning techniques that further optimize query efficiency. Experimental comparison with the state-of-the-art index demonstrates the superiority of PCSP. It can answer each LCSP query in around 100 microseconds and runs faster than the best-known solution by up to two orders of magnitude.

Recently more and more people focus on continuous K-Nearest Neighbor (CKNN) query processing over moving Objects in Road Networks. A CKNN query is to find among all moving objects the K-nearest neighbors (KNNs) of a moving query object during a period of time. The main issue with existing methods is that moving objects change their locations frequently over time and if their location updates cannot be processed in time, the system runs the risk of retrieving the incorrect results of KNN. In this paper, an effective method is proposed to deal with continuous K-Nearest Neighbor query processing. By considering whether a moving object o is moving farther away from or getting closer to a query point q, the object which is definitely not in the KNN result set is effectively excluded. Thus we can reduce the communication cost, meanwhile we can also simplify the network distance computation between moving objects and query q. Comprehensive experiments are conducted and the results verify the effectiveness of the proposed algorithms.

SummaryIn recent times, many location‐based service providers (LBSPs) choose to outsource data query services to third‐party cloud service providers (CSPs). This allows users to easily search for points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in‐vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead.

Proposed a new index structure, named MG2R*, can efficiently store and retrieve the past, present and future positions of network-constrained moving objects. It is a two-tier structure. The upper is a MultiGrid-R*-Tree (MGRT for short) that is used to index the road network. The lower is a group of independent R*-Tree. Each R*-Tree is relative to a route in the road network, can index the spatiotemporal trajectory of the moving objects in the road. Moreover, moving objects query is implemented based on this index structure. It compared to other index structures for road-network-based moving objects, such as MON-Tree, the experimental results shown that the MG2R* can effectively improve the query performance of the spatio-temporal trajectory of network-constrained moving objects.

A set of distributed continual range query requests, each defining a geographical region of interest, needs to be periodically reevaluated to provide up to date answers. Processing these continual queries efficiently and incrementally becomes important for location based services and applications. In this paper, we propose an efficient incremental method for continuous range query characterized by mobility of objects which follow paths in an underlying spatial road network. We notice that both queries and moving objects can be attached to the road link for updating of query answers. By using Grid R∗-tree which indexing spatial road network data, query requests and moving objects register themselves to corresponding road links according to query areas and moving object locations on the network. Also we analyzed the impacts to query answers caused by geometry relation of road link and query area and state of moving objects. Then we proposed an efficient update algorithm and corresponding memory data structures based on spatial and state classification which formed a safe region to avoid unnecessary updates. Finally a comprehensive experimental evaluation using real data has been conducted to demonstrate the efficiency and effectiveness of our algorithm.

K-Aggregate Nearest Neighbor Query Method of Moving Objects in Road Networks