Continuous visible nearest neighbor queries

Gao, Yunjun; Zheng, Baihua; Lee, Wang-Chien; Chen, Gencai

doi:10.1145/1516360.1516378

Cited by 41 publications

(36 citation statements)

References 37 publications

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“…In recent years, other types of queries on moving objects have also been studied extensively. These include the range queries [1,26], the kNN queries with two predicates [2], the density queries [10], the intersection join queries [27,28], the obstructed NN queries [5,13], the visible NN queries [6], the weighted NN queries [14] and the destination prediction queries [24], etc. These studies have different problem settings from ours and their solutions are inapplicable.…”

Section: Related Workmentioning

confidence: 99%

Processing moving k NN queries using influential neighbor sets

et al. 2014

Proc. VLDB Endow.

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The moving k nearest neighbor query, which computes one's k nearest neighbor set and maintains it while at move, is gaining importance due to the prevalent use of smart mobile devices such as smart phones. Safe region is a popular technique in processing the moving k nearest neighbor query. It is a region where the movement of the query object does not cause the current k nearest neighbor set to change. Processing a moving k nearest neighbor query is a continuing process of checking the validity of the safe region and recomputing it if invalidated. The size of the safe region largely decides the frequency of safe region recomputation and hence query processing efficiency. Existing moving k nearest neighbor algorithms lack efficiency due to either computing small safe regions and have to recompute frequently or computing large safe regions (i.e., an order-k Voronoi cell) with a high cost.In this paper, we take a third approach. Instead of safe regions, we use a small set of safe guarding objects. We prove that, as long as the the current k nearest neighbors are closer to the query object than the safe guarding objects, the current k nearest neighbors stay valid and no recomputation is required. This way, we avoid the high cost of safe region recomputation. We also prove that, the region defined by the safe guarding objects is the largest possible safe region. This means that the recomputation frequency of our method is also minimized. We conduct extensive experiments comparing our method with the state-of-the-art method on both real and synthetic data sets. The results confirm the superiority of our method.

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Section: Related Workmentioning

confidence: 99%

Processing moving k NN queries using influential neighbor sets

et al. 2014

Proc. VLDB Endow.

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“…Relevant to our work, spatial queries for selecting a set of spatial points, aiming to minimize the total spatial distance, have been proposed for various scenarios [5,6,8,9]. However, in these works, the (social) connectivity among the spatial points is not considered.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, given two sets of points P and Q, together with the number of points to be selected k, Group Nearest Neighbor Query [5] finds a set of k points in P such that the total spatial distance of the points to all points in Q is minimized. On the other hand, for a line segment and a set of points, Continuous Nearest Neighbor Search [6] returns the nearest neighbor of each point on the line segment, while the Continuous Visible Nearest Neighbor Queries [8] extends Continuous Nearest Neighbor Search [6] by incorporating the obstacles in the problem design, which may affect the visibility or distance between two points and lead to different results. Meanwhile, Continuous Obstructed Nearest Neighbor Query [9] retrieves the nearest neighbor with regard to the obstructed distance, i.e., the shortest path without crossing any obstacle.…”

Section: Related Workmentioning

confidence: 99%

On socio-spatial group query for location-based social networks

Yang

Shen

Lee

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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112

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Challenges faced in organizing impromptu activities are the requirements of making timely invitations in accordance with the locations of candidate attendees and the social relationship among them. It is desirable to find a group of attendees close to a rally point and ensure that the selected attendees have a good social relationship to create a good atmosphere in the activity. Therefore, this paper proposes Socio-Spatial Group Query (SSGQ) to select a group of nearby attendees with tight social relation. Efficient processing of SSGQ is very challenging due to the tradeoff in the spatial and social domains. We show that the problem is NP-hard via a proof and design an efficient algorithm SSGSelect, which includes effective pruning techniques to reduce the running time for finding the optimal solution. We also propose a new index structure, Social R-Tree to further improve the efficiency. User study and experimental results demonstrate that SSGSelect significantly outperforms manual coordination in both solution quality and efficiency.

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“…Nutanong et al [15] explore the visible k-nearest neighbor (VkNN) search, which returns the k NNs that are visible to a specified query point. Further studies along this line include visible reverse k-nearest neighbor search [8] and continuous VkNN search [9].…”

Section: Queries With Obstaclesmentioning

confidence: 99%

Continuous obstructed nearest neighbor queries in spatial databases

Gao

Zheng

2009

Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data

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In this paper, we study a novel form of continuous nearest neighbor queries in the presence of obstacles, namely continuous obstructed nearest neighbor (CONN) search. It considers the impact of obstacles on the distance between objects, which is ignored by most of spatial queries. Given a data set P, an obstacle set O, and a query line segment q in a two-dimensional space, a CONN query retrieves the nearest neighbor of each point on q according to the obstructed distance, i.e., the shortest path between them without crossing any obstacle. We formulate CONN search, analyze its unique properties, and develop algorithms for exact CONN query processing, assuming that both P and O are indexed by conventional data-partitioning indices (e.g., R-trees). Our methods tackle the CONN retrieval by performing a single query for the entire query segment, and only process the data points and obstacles relevant to the final result, via a novel concept of control points and an efficient quadraticbased split point computation algorithm. In addition, we extend our solution to handle the continuous obstructed k-nearest neighbor (COkNN) search, which finds the k (≥ 1) nearest neighbors to every point along q based on obstructed distances. A comprehensive experimental evaluation using both real and synthetic datasets has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms.

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Continuous visible nearest neighbor queries

Cited by 41 publications

References 37 publications

Processing moving k NN queries using influential neighbor sets

Processing moving k NN queries using influential neighbor sets

On socio-spatial group query for location-based social networks

Continuous obstructed nearest neighbor queries in spatial databases

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