Efficient Evaluation of k-Range Nearest Neighbor Queries in Road Networks

Abstract

Abstract-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.

LARS: A Location-Aware Recommender System

“…To calculate an exact travel penalty for a user u to item i, we employ an incremental k-nearest-neighbor (KNN) technique [18], [19], [20]. Given a user location l, incremental KNN algorithms return, on each invocation, the next item i nearest to u with regard to travel distance d. In our case, we normalize distance d to the ratings scale to get the travel penalty in Equation 5.…”

“…Given a user location l, incremental KNN algorithms return, on each invocation, the next item i nearest to u with regard to travel distance d. In our case, we normalize distance d to the ratings scale to get the travel penalty in Equation 5. Incremental KNN techniques exist for both Euclidean distance [19] and (road) network distance [18], [20]. The advantage of using Incremental KNN techniques is that they provide an exact travel distances between a querying user's location and each recommendation candidate item.…”

Top-k Spatial Preference Queries in Directed Road Networks

“…Thus, given a query point q, we are mainly interested in finding the set of nearest objects to it that meet a specific condition. Many algorithms [1,[13][14][15] have been proposed to retrieve the nearest neighbor objects both in Euclidean space and road networks.…”

“…According to Skyhook, the number of location-based applications being developed each month is increasing exponentially. Thus, spatial queries such as k nearest neighbor, range queries and reverse nearest neighbor [1][2][3][4][5] have received a significant amount of attention from the research community. However, most of the existing applications are limited to traditional spatial queries, which return objects based on their distances from the query point.…”

Trajectory privacy in location-based services and data publication

“…In this framework, a privacy-aware query processor is embedded in the database server to deal with the cloaked spatial area received either from a querying user [5; 29] or from a trusted third party [9; 31; 40]. For spatial cloaking in road networks, an efficient and query-aware algorithm is proposed to process privacyaware location-based queries [3].…”