Continuous medoid queries over moving objects

Papadopoulos, S; Sacharidis, D; Mouratidis, K

dc.contributor.author	Papadopoulos, S	en
dc.contributor.author	Sacharidis, D	en
dc.contributor.author	Mouratidis, K	en
dc.date.accessioned	2014-03-01T02:44:32Z
dc.date.available	2014-03-01T02:44:32Z
dc.date.issued	2007	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31866
dc.subject	Continuous query processing	en
dc.subject	Medoid queries	en
dc.subject	Moving object databases	en
dc.subject.other	Approximation algorithms	en
dc.subject.other	Computational efficiency	en
dc.subject.other	Integrated control	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Continuous query processing	en
dc.subject.other	Medoid queries	en
dc.subject.other	Moving object databases	en
dc.subject.other	Query languages	en
dc.title	Continuous medoid queries over moving objects	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-73540-3_3	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-73540-3_3	en
heal.publicationDate	2007	en
heal.abstract	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 that the data points have some computational capabilities, and they take over part of the monitoring task. In particular, the server installs adaptive filters (i.e., permissible spatial ranges, called safe regions) to the points, which report their location only when they move outside their filters. The distributed techniques reduce the frequency of location updates (and, thus, the network overhead and the server load), at the cost of a slightly higher average distance, compared to the centralized methods. Both our centralized and distributed methods do not make any assumption about the data moving patterns (e.g., velocity vectors, trajectories, etc) and can be applied to an arbitrary number of medoids k. We demonstrate the efficiency and efficacy of our techniques through extensive experiments. © Springer-Verlag Berlin Heidelberg 2007.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-73540-3_3	en
dc.identifier.volume	4605 LNCS	en
dc.identifier.spage	38	en
dc.identifier.epage	56	en