Abstract

In this paper, we present an efficient in-DBMS framework for progressive time-aware sub-trajectory cluster analysis. In particular, we address two variants of the problem: (a) spatiotemporal sub-trajectory clustering and (b) index-based time-aware clustering at querying environment. Our approach for (a) relies on a two-phase process: a voting-and-segmentation phase followed by a sampling-and-clustering phase. Regarding (b), we organize data into partitions that correspond to groups of sub-trajectories, which are incrementally maintained in a hierarchical structure. Both approaches have been implemented in Hermes@PostgreSQL, a real Moving Object Database engine built on top of PostgreSQL, enabling users to perform progressive cluster analysis via simple SQL. The framework is also extended with a Visual Analytics (VA) tool to facilitate real world analysis.

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Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1109/icde.2018.00181
https://openaccess.city.ac.uk/id/eprint/23212,
https://ieeexplore.ieee.org/document/8509402,
https://doi.org/10.1109/ICDE.2018.00181,
https://academic.microsoft.com/#/detail/2898552906
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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.1109/icde.2018.00181
Licence: Other

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