Skip to main content

A Method for Continuous Clustering and Querying of Moving Objects

  • 585 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1075)


Location sensing moving objects generate a continuous stream of spatio-temporal data. Querying and analysis of this data can give more inference on the mobility patterns of the objects. In this paper, we are proposing a method for evaluating spatio-temporal aggregate queries. For the effective processing of queries continuous clustering is employed on moving objects. The frequency of clustering is determined by the semantic properties of the travel network. Moving objects and queries are combined together and processed incrementally all through the travel network. The cluster membership of an object may change during the course of the journey. By analyzing this, the pattern of the movement of object can be ascertained. Special data structures are maintained to keep track clusters to answer spatio-temporal aggregate queries. We prove that our system can deliver answers to spatio-temporal aggregate queries effectively without processing the entire records of the moving objects.


  • Location based systems
  • Moving objects
  • Semantic data processing
  • Spatio-temporal data mining
  • Spatio-temporal aggregate queries

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. Alvares, L.O., Bogorny, V., Kuijpers, B., de Macedo, J.A.F., Moelans, B., Vaisman, A.: A model for enriching trajectories with semantic geographical information. In: Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, p. 22. ACM (2007)

    Google Scholar 

  2. Chen, J., Lai, C., Meng, X., Xu, J., Hu, H.: Clustering moving objects in spatial networks. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 611–623. Springer, Heidelberg (2007).

    CrossRef  Google Scholar 

  3. Gryllakis, F., Pelekis, N., Doulkeridis, C., Sideridis, S., Theodoridis, Y.: Searching for spatio-temporal-keyword patterns in semantic trajectories. In: Adams, N., Tucker, A., Weston, D. (eds.) IDA 2017. LNCS, vol. 10584, pp. 112–124. Springer, Cham (2017).

    CrossRef  Google Scholar 

  4. Jensen, C.S., Lin, D., Ooi, B.C.: Continuous clustering of moving objects. IEEE Trans. Knowl. Data Eng. 19(9), 1161–1174 (2007)

    CrossRef  Google Scholar 

  5. Li, Y., Han, J., Yang, J.: Clustering moving objects. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 617–622. ACM (2004)

    Google Scholar 

  6. Li, Z., Lee, J.-G., Li, X., Han, J.: Incremental clustering for trajectories. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5982, pp. 32–46. Springer, Heidelberg (2010).

    CrossRef  Google Scholar 

  7. Nehme, R.V., Rundensteiner, E.A.: SCUBA: scalable cluster-based algorithm for evaluating continuous spatio-temporal queries on moving objects. In: Ioannidis, Y., et al. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 1001–1019. Springer, Heidelberg (2006).

    CrossRef  Google Scholar 

  8. Nishad, A., Abraham, S.: Semantic trajectory analysis for identifying locations of interest of moving objects. In: 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), pp. 257–261. IEEE (2017)

    Google Scholar 

  9. Niyizamwiyitira, C., Lundberg, L.: Performance evaluation of trajectory queries on multiprocessor and cluster. Comput. Sci. Inf. Technol. Vienna Austria 6, 145–163 (2016)

    Google Scholar 

  10. Portugal, I., Alencar, P., Cowan, D.: Developing a spatial-temporal contextual and semantic trajectory clustering framework. arXiv preprint arXiv:1712.03900 (2017)

  11. Xu, J., Lu, H., Güting, R.H.: Range queries on multi-attribute trajectories. IEEE Trans. Knowl. Data Eng. 30(6), 1206–1211 (2018)

    CrossRef  Google Scholar 

  12. Young, S., Arel, I., Karnowski, T.P., Rose, D.: A fast and stable incremental clustering algorithm. In: 2010 Seventh International Conference on Information Technology: New Generations, pp. 204–209. IEEE (2010)

    Google Scholar 

  13. Yu, Y., Wang, Q., Wang, X., Wang, H., He, J., et al.: Online clustering for trajectory data stream of moving objects. Comput. Sci. Inf. Syst. 10(3), 1293–1317 (2013)

    CrossRef  Google Scholar 

  14. Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 316–324. ACM (2011)

    Google Scholar 

  15. Yuan, J., et al.: T-drive: driving directions based on Taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 99–108. ACM (2010)

    Google Scholar 

  16. Zheng, B., Yuan, N.J., Zheng, K., Xie, X., Sadiq, S., Zhou, X.: Approximate keyword search in semantic trajectory database. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 975–986. IEEE (2015)

    Google Scholar 

  17. Zhou, P., Zhang, D., Salzberg, B., Cooperman, G., Kollios, G.: Close pair queries in moving object databases. In: Proceedings of the 13th Annual ACM International Workshop on Geographic Information Systems, pp. 2–11. ACM (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to A. Nishad .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nishad, A., Abraham, S. (2019). A Method for Continuous Clustering and Querying of Moving Objects. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0107-4

  • Online ISBN: 978-981-15-0108-1

  • eBook Packages: Computer ScienceComputer Science (R0)