Monitoring Aggregate k-NN Objects in Road Networks

Abstract. 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.

• Type: Conference
• Year: 2008
• Where: SSDBM
• Authors: Lu Qin, Jeffrey Xu Yu, Bolin Ding, Yoshiharu Ishikawa
• Keywords: Database, Multiple Query Points, Query Points, Road Networks, SSDBM 2008