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.
|Published in||Proceedings of the 20th International Conference on Scientific and Statistical Database Management (SSDBM 2008)|