Continuous Medoid Queries over Moving Objects

In the k-medoid problem, given a dataset P, we are asked to choose k points in P as the medoids. The optimal medoid set minimizes the average Euclidean distance between the points in P and their closest medoid. Finding the optimal k medoids is NP hard, and existing algorithms aim at approximate answers, i.e., they compute medoids that achieve a small, yet not minimal, average distance. Similarly in this paper, we also aim at approximate solutions. We consider, however, the continuous version of the problem, where the points in P move and our task is to maintain the medoid set on-the-fly (trying to keep the average distance small). To the best of our knowledge, this work constitutes the first attempt on continuous medoid queries. First, we consider centralized monitoring, where the points issue location updates whenever they move. A server processes the stream of generated updates and constantly reports the current medoid set. Next, we address distributed monitoring, where we assume tha...

• Type: Conference
• Year: 2007
• Where: SSD
• Authors: Stavros Papadopoulos, Dimitris Sacharidis, Kyriakos Mouratidis
• Keywords: Average Distance, Average Euclidean Distance, Medoid Set, SSD 2007