Processing moving k NN queries using influential neighbor sets

Abstract

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.

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

“…In many service networks (e.g., wireless networks), space is divided into Voronoi polygons around the service points [10]. Applying Voronoi diagrams or grids are also common approaches for indexing moving objects in spatial databases [20,21]. Although the proposed method is not built on a presumed zoning approach, constant interval grids with cell sizes of 25 m and 50 m (Figure 7a) and Voronoi polygons (Figure 7b) are the basis of our analysis so that some usual zonings are taken into consideration.…”

“…We did not derive an analytical presentation for Equation (20); instead, we applied a numerical integration method. We used rectangular integration, which is an efficient method for the integration of polynomial functions [18].…”

Dual Space-Time Representation

“…The main difference between the samplingbased method (Song and Roussopoulos 2001) and the safe region-based method (Li et al 2014;Nutanong et al 2010) can be described as follows. The sampling-based method (Song and Roussopoulos 2001) does not produce truly continuous query answers; its main objective is to reduce the cost of each kNN query execution in order to accommodate a higher query frequency.…”

“…The sampling-based method (Song and Roussopoulos 2001) does not produce truly continuous query answers; its main objective is to reduce the cost of each kNN query execution in order to accommodate a higher query frequency. On the other hand, the safe region-based method (Li et al 2014;Nutanong et al 2010) can produce continuous query results by keeping track of locations at which the query point crosses a safe region boundary.…”

Efficient spatial keyword query processing on geo-textual data

“…Although some existing works adopt safe region technique to reduce the query processing cost [62,74], they fall short either in the region construction overhead or validation overhead. Therefore, on-going efforts are required to meliorate the user experience by improving the continuous spatial keyword query processing efficiency.…”