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|Title:||Effective online group discovery in trajectory databases||Authors:||Li, X.
|Issue Date:||2013||Citation:||Li, X., Čeikute, V., Jensen, C.S., Tan, K.-L. (2013). Effective online group discovery in trajectory databases. IEEE Transactions on Knowledge and Data Engineering 25 (12) : 2752-2766. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2012.193||Abstract:||GPS-enabled devices are pervasive nowadays. Finding movement patterns in trajectory data stream is gaining in importance. We propose a group discovery framework that aims to efficiently support the online discovery of moving objects that travel together. The framework adopts a sampling-independent approach that makes no assumptions about when positions are sampled, gives no special importance to sampling points, and naturally supports the use of approximate trajectories. The framework's algorithms exploit state-of-the-art, density-based clustering (DBScan) to identify groups. The groups are scored based on their cardinality and duration, and the top-k groups are returned. To avoid returning similar subgroups in a result, notions of domination and similarity are introduced that enable the pruning of low-interest groups. Empirical studies on real and synthetic data sets offer insight into the effectiveness and efficiency of the proposed framework. © 1989-2012 IEEE.||Source Title:||IEEE Transactions on Knowledge and Data Engineering||URI:||http://scholarbank.nus.edu.sg/handle/10635/77844||ISSN:||10414347||DOI:||10.1109/TKDE.2012.193|
|Appears in Collections:||Staff Publications|
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