Lu Qin, Jeffrey Xu Yu, Bolin Ding, and Yoshiharu Ishikawa
In recent years, there is an increasing need to monitor k nearest neighbor (k-NN) in a road network. There are existing solutions on either monitoring k-NN objects from a single query point over a road network, or computing the snapshot k-NN objects over a road network to minimize an aggregate distance function with respect to multiple query points. In this paper, we study a new problem that is to monitor k-NN objects over a road network from multiple query points to minimize an aggregate distance function with respect to the multiple query points. We call it a continuous aggregate k-NN (CANN) query. We propose a new approach that can signiﬁcantly reduce the cost of computing network distances when monitoring aggregate k-NN objects on road networks. We conducted extensive experimental studies and conﬁrmed the efﬁciency of our algorithms.
In Proceedings of the 20th International Conference on Scientific and Statistical Database Management (SSDBM 2008)