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Data Mining and Knowledge Discovery

, Volume 31, Issue 5, pp 1294–1330 | Cite as

On temporal-constrained sub-trajectory cluster analysis

  • Nikos Pelekis
  • Panagiotis Tampakis
  • Marios Vodas
  • Christos Doulkeridis
  • Yannis Theodoridis
Article
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2017

Abstract

Cluster analysis over Moving Object Databases (MODs) is a challenging research topic that has attracted the attention of the mobility data mining community. In this paper, we study the temporal-constrained sub-trajectory cluster analysis problem, where the aim is to discover clusters of sub-trajectories given an ad-hoc, user-specified temporal constraint within the dataset’s lifetime. The problem is challenging because: (a) the time window is not known in advance, instead it is specified at query time, and (b) the MOD is continuously updated with new trajectories. Existing solutions first filter the trajectory database according to the temporal constraint, and then apply a clustering algorithm from scratch on the filtered data. However, this approach is extremely inefficient, when considering explorative data analysis where multiple clustering tasks need to be performed over different temporal subsets of the database, while the database is updated with new trajectories. To address this problem, we propose an incremental and scalable solution to the problem, which is built upon a novel indexing structure, called Representative Trajectory Tree (ReTraTree). ReTraTree acts as an effective spatio-temporal partitioning technique; partitions in ReTraTree correspond to groupings of sub-trajectories, which are incrementally maintained and assigned to representative (sub-)trajectories. Due to the proposed organization of sub-trajectories, the problem under study can be efficiently solved as simply as executing a query operator on ReTraTree, while insertion of new trajectories is supported. Our extensive experimental study performed on real and synthetic datasets shows that our approach outperforms a state-of-the-art in-DBMS solution supported by PostgreSQL by orders of magnitude.

Keywords

Cluster analysis Temporal-constrained (sub-)trajectory clustering Moving objects Indexing 

Notes

Acknowledgements

This work was partially supported by project datACRON, which has received funding from the European Union’s Horizon 2020 research and innovation Programme under Grant Agreement No 687591. The work of P. Tampakis and C. Doulkeridis has been co-financed by ESF and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: Aristeia II, Project: ROADRUNNER.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Department of Statistics and Insurance ScienceUniversity of PiraeusPiraeusGreece
  2. 2.Department of InformaticsUniversity of PiraeusPiraeusGreece
  3. 3.Department of Digital SystemsUniversity of PiraeusPiraeusGreece

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