CkNN monitoring based on parallel pre-computing

The problem of k nearest neighbor (kNN) queries plays an important role in spatial information retrieval. The continuous k nearest neighbor query is a variation of kNN query which is aimed to find the kNN in a given path for a query point continuously. Recently, The problem of CkNN queries over moving objects in road networks has caught more and more researchers´ attention due to its various applications. In this paper, we report on a pre-processing based approach to answer CkNN queries with light online computation cost. We evaluate our approach on a real data set. The evaluation results validate the effectiveness and efficiency of our approach. Besides, we design a prototype system for monitoring and navigating the urban taxis based on CkNN queries. In our system, we utilize the parallel pre-computing and approximation techniques to support a large number of moving objets. Through a web-based graphical interface, both taxi drivers and pedestrians can access our system and query for their CkNN.