Time-Aware Similarity Search: A Metric-Temporal Representation for Complex Data
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Efficiently detecting clusters of mobile objects in the presence of dense noise
Proceedings of the 2010 ACM Symposium on Applied Computing
An adaptive updating protocol for reducing moving object database workload
Proceedings of the VLDB Endowment
An adaptive updating protocol for reducing moving object database workload
The VLDB Journal — The International Journal on Very Large Data Bases
Aggregating and disaggregating flexibility objects
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Dynamic k-means: a clustering technique for moving object trajectories
International Journal of Intelligent Information and Database Systems
Optimal k-constraint coverage queries on spatial objects
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
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Given a dataset P, a k-means query returns k points in space (called centers), such that the average squared distance between each point in P and its nearest center is minimized. Since this problem is NP-hard, several approximate algorithms have been proposed and used in practice. In this paper, we study continuous k-means computation at a server that monitors a set of moving objects. Re-evaluating k-means every time there is an object update imposes a heavy burden on the server (for computing the centers from scratch) and the clients (for continuously sending location updates). We overcome these problems with a novel approach that significantly reduces the computation and communication costs, while guaranteeing that the quality of the solution, with respect to the re-evaluation approach, is bounded by a user-defined tolerance. The proposed method assigns each moving object a threshold (i.e., range) such that the object sends a location update only when it crosses the range boundary. First, we develop an efficient technique for maintaining the k-means. Then, we present mathematical formulae and algorithms for deriving the individual thresholds. Finally, we justify our performance claims with extensive experiments.