This paper proposes a fast safe-region generation method for several kinds of vicinity queries including set k nearest neighbor (NN) queries, ordered kNN queries, reverse kNN queries, and distance range queries. When a user is driving a car on a road network, he/she wants to know about objects located in the vicinity of the car. However, the result changes according to the movement of the car, and therefore, the user needs to request up-to-date result to the server. On the other hand, frequent requests for up-to-date results cause heavy loadings on the server. To cope with this problem efficiently, the idea of the safe-region has been proposed, however, it takes long processing time in existing works. This paper proposes a fast generation method of the safe-region applicable to several types of vicinity queries. Through experimental evaluations, we demonstrate that the proposed method outperforms the existing algorithms in the processing time by one or two orders of magnitude.

In this paper, we propose a communication-efficient top-k continuous query processing method on distributed local nodes where data are horizontally partitioned. A designated coordinator server takes the role of issuing queries from users to local nodes and delivering the results to users. The final results are requested via a top-k subscription which lets local nodes know which data and updates need to be returned to users. Our proposed method makes use of the active previously posed queries to identify a small set of needed top-k subscriptions. In addition, with the pre-indexed nodes' skylines, the number of local nodes to be subscribed can be significantly reduced. As a result, only a small number of subscriptions are informed to a small number of local nodes resulting in lower communication overhead. Furthermore, according to dynamic data updates, we also propose a method that prevents nodes from reporting needless updates and also maintenance procedures to preserve the consistency. The results of experiments that measure the volume of transferred data show that our proposed method significantly outperforms the previously proposed methods.

In this paper, we propose a continuous range query processing method over moving objects. To efficiently process continuous range queries, we design a main-memory-based query index that uses smaller storage and significantly reduces the query processing time. We show through performance evaluation that the proposed method outperforms the existing methods.