Voronoi-based aggregate nearest neighbor query processing in road networks

Developed from Nearest Neighbor (NN) queries, Aggregate Nearest Neighbor (ANN) queries return the object that minimizes an aggregate distance function with respect to a set of query points. Because of the multiple query points, ANN queries are much more complex than NN queries. For optimizing the query processing and improving the query efficiency, many ANN queries algorithms utilizes pruning strategies, with or without an index structure. Obviously, the pruning effect highly depends on the tightness of the bound estimation. In this paper, we figure out a property in vector space and develop some efficient bound estimations for two most popular types of ANN queries. Based on these bounds, we design the indexed and non-index ANN algorithms, and conduct experimental studies. Our algorithms show good performance, especially for high dimensional queries, for both real dataset and synthetic datasets.

Network Voronoi Diagram on uncertain objects for nearest neighbor queries

Given a group $\mathcal {Q}$ of query points and a set $\mathcal {P}$ of points of interest (POIs), aggregate nearest neighbor (ANN) queries find a POI p from $\mathcal {P}$ that achieves the smallest aggregate distance. Specifically, the aggregate distance is defined over the set of distances between p and all query points in $\mathcal {Q}$ . Existing studies on ANN query mainly consider the spatial proximity, whereas the textual similarity has received considerable attention recently. In this work, we utilize user-specified query keywords to capture textual similarity. We study the aggregate keyword nearest neighbor (AKNN) queries, finding the POI that has the smallest aggregate distance and covers all query keywords. Nevertheless, existing methods on ANN query are either inapplicable or inefficient when applied to the AKNN query. To answer our query efficiently, we first develop a dual-granularity (DG) indexing schema. It preserves abstracts of the road network by a tree structure, and preserves detailed network information by an extended adjacency list. Then, we propose a minimal first search (MFS) algorithm. It traverses the tree and explores the node with the minimal aggregate distance iteratively. This method suffers from false hits arising from keyword tests. Thus, we propose the collaborative filtering technique, which performs keywords test by multiple keyword bitmaps collectively rather than by only one. Extensive experiments on both real and synthetic datasets demonstrate the superiority of our algorithms and optimizing strategies.

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.

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.

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.

The k nearest neighbor (KNN) query, an essential query for information processing in sensor networks, has not received sufficient attention in the research community of sensor networks. In this paper, we examine in-network processing of KNN queries by proposing two alternative algorithms, namely the GeoRouting Tree (GRT) and the KNN Boundary Tree (KBT). The former is based on a distributed spatial index structure and prunes off the irrelevant nodes during query propagation. The latter is based upon ad-hoc geographic routing and first obtains a region within which at least k nearest sensor nodes are enclosed and then decides the k nearest nodes to the query point. We provide an extensive performance evaluation to study the impact of various system factors and protocol parameters. Our results show that GRT yields a good tradeoff between energy consumption and query accuracy in static scenarios. On the other hand, KBT achieves better energy efficiency while being more tolerant to network dynamics.

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.

Fast optimal aggregate point search for a merged set on road networks

On multi-type reverse nearest neighbor search

A collaborative approach to moving [formula omitted]-nearest neighbor queries in directed and dynamic road networks

The great advances in deep learning enable human beings to extract the semantic information in ubiquitous unstructured data into high-dimensional vectors, leading to an urgent priority of managing vector data. Nearest neighbor (NN) query is a pivotal question in vector data management. Unfortunately, the NN query has been proved to be intrinsically hard. Researchers, thus, turn to approximate nearest neighbor (ANN) query which targets better efficiency by slightly compromising accuracy. Despite this, existing ANN methods still incur high time and space costs, which are mainly due to the high dimensionality. Specifically, in most in-memory ANN algorithms, the time cost is dominated by the time for comparing the distances among vectors. Each distance comparison operation (DCO) involves hundreds of arithmetic operations as each vector has hundreds of dimensions. Similarly, the space cost is dominated by the space for storing the vectors. Towards the goal of reducing the time and space costs, we present three methods in this thesis. First, we observe that, for a DCO, if an approximate distance can confirm that a vector is not a NN, then it can be discarded without computing exact distances. Based on this observation, we propose a method named ADSampling. For a DCO, it iteratively and incrementally samples a few dimensions and computes an approximate distance. When this distance is accurate enough to decide the result of the DCO, it terminates sampling. Second, to further improve the efficiency of DCOs, we design an efficient distance estimator. We propose a new quantization method named RaBitQ, which maps $D$-dimensional vectors into $D$-bit strings. RaBitQ provides an unbiased and asymptotically optimal estimator with good empirical accuracy. In addition, we introduce efficient implementations of RaBitQ based on bitwise operations or SIMD-based operations. Third, by extending the RaBitQ method, we develop an algorithm which compresses a floating-point vector into a $B$-bit unsigned integer vector. We prove that our algorithm is asymptotically optimal in terms of the space-accuracy trade-off.

In this paper, we study the problem of continuous monitoring of reverse k nearest neighbors queries in Euclidean space as well as in spatial networks. Existing techniques are sensitive toward objects and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We present a framework for continuous reverse k nearest neighbor (RkNN) queries by assigning each object and query with a safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions. This significantly improves the computation cost. As a byproduct, our framework also reduces the communication cost in client---server architectures because an object does not report its location to the server unless it leaves its safe region or the server sends a location update request. We also conduct a rigid cost analysis for our Euclidean space RkNN algorithm. We show that our techniques can also be applied to answer bichromatic RkNN queries in Euclidean space as well as in spatial networks. Furthermore, we show that our techniques can be extended for the spatial networks that are represented by directed graphs. The extensive experiments demonstrate that our techniques outperform the existing techniques by an order of magnitude in terms of computation cost and communication cost.

Aggregate nearest neighbor (ANN) search retrieves for two spatial datasets T and Q, segment(s) of one or more trajectories from the set T having minimum aggregate distance to points in Q. When interacting with large amounts of trajectories, this process would be very time-consuming due to consecutive page loads. An approximate method for finding segments with minimum aggregate distance is proposed which can improve the response time. In order to index large volumes of trajectories, scalable and efficient trajectory index (SETI) structure is used. But some refinements are provided to temporal index of SETI to improve the performance of proposed method. The experiments were performed with different number of query points and percentages of dataset. It is shown that proposed method besides having an acceptable precision, can reduce the computation time significantly. It is also shown that the main fraction of search time among load time, ANN and computing convex and centroid, is related to ANN.

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.