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Time-aware sub-trajectory clustering in HERMES@PostgreSQL

: Tampakis, P.; Pelekis, N.; Theodoridis, Y.; Andrienko, N.; Andrienko, G.; Fuchs, G.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 34th International Conference on Data Engineering, ICDE 2018. Proceedings : 16 - 19 April 2018, Paris, France
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2018
ISBN: 978-1-5386-5520-7
ISBN: 978-1-5386-5521-4
International Conference on Data Engineering (ICDE) <34, 2018, Paris>
Conference Paper
Fraunhofer IAIS ()

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.