Nothing Special   »   [go: up one dir, main page]

Skip to main content

Detecting Regular Visit Patterns

  • Conference paper
Algorithms - ESA 2008 (ESA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5193))

Included in the following conference series:

Abstract

We are given a trajectory \(\mathcal{T}\) and an area \(\mathcal{A}\). \(\mathcal{T}\) might intersect \(\mathcal{A}\) several times, and our aim is to detect whether \(\mathcal{T}\) visits \(\mathcal{A}\) with some regularity, e.g. what is the longest time span that a GPS-GSM equipped elephant visited a specific lake on a daily (weekly or yearly) basis, where the elephant has to visit the lake most of the days (weeks or years), but not necessarily on every day (week or year).

During the modelling of such applications, we encounter an elementary problem on bitstrings, that we call LDS (LongestDenseSubstring). The bits of the bitstring correspond to a sequence of regular time points, in which a bit is set to 1 iff the trajectory \(\mathcal{T}\) intersects the area \(\mathcal{A}\) at the corresponding time point. For the LDS problem, we are given a string s as input and want to output a longest substring of s, such that the ratio of 1’s in the substring is at least a certain threshold.

In our model, LDS is a core problem for many applications that aim at detecting regularity of \(\mathcal{T}\) intersecting \(\mathcal{A}\). We propose an optimal algorithm to solve LDS, and also for related problems that are closer to applications, we provide efficient algorithms for detecting regularity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 189.00
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Wildlife tracking projects with GPS GSM collars (2006), http://www.environmental-studies.de/projects/projects.html

  2. Agarwal, P., Erickson, J.: Geometric range searching and its relatives (1999)

    Google Scholar 

  3. Al-Naymat, G., Chawla, S., Gudmundsson, J.: Dimensionality reduction for long duration and complex spatio-temporal queries. In: Proceedings of the 22nd ACM Symposium on Applied Computing, pp. 393–397. ACM, New York (2007)

    Google Scholar 

  4. Andersson, M., Gudmundsson, J., Laube, P., Wolle, T.: Reporting leaders and followers among trajectories of moving point objects. GeoInformatica (2007)

    Google Scholar 

  5. Benkert, M., Gudmundsson, J., Hübner, F., Wolle, T.: Reporting flock patterns. Computational Geometry—Theory and Applications (2007)

    Google Scholar 

  6. Frank, A.U.: Socio-Economic Units: Their Life and Motion. In: Frank, A.U., Raper, J., Cheylan, J.P. (eds.) Life and motion of socio-economic units. GISDATA, vol. 8, pp. 21–34. Taylor & Francis, London (2001)

    Google Scholar 

  7. Gudmundsson, J., Laube, P., Wolle, T.: Encyclopedia of GIS, chapter Movement Patterns in Spatio-temporal Data, pp. 726–732. Springer, Heidelberg (2008)

    Google Scholar 

  8. Gudmundsson, J., van Kreveld, M.: Computing longest duration flocks in trajectory data. In: Proceedings of the 14th ACM Symposium on Advances in GIS, pp. 35–42 (2006)

    Google Scholar 

  9. Gudmundsson, J., van Kreveld, M., Speckmann, B.: Efficient detection of motion patterns in spatio-temporal sets. GeoInformatica 11(2), 195–215 (2007)

    Article  Google Scholar 

  10. Güting, R.H., Schneider, M.: Moving Objects Databases. Morgan Kaufmann Publishers, San Francisco (2005)

    Google Scholar 

  11. Lee, J.-G., Han, J., Whang, K.-Y.: Trajectory clustering: a partition-and-group framework. In: SIGMOD 2007: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, pp. 593–604. ACM Press, New York (2007)

    Chapter  Google Scholar 

  12. Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.: Mining, indexing, and querying historical spatiotemporal data. In: Proceedings of the 10th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, pp. 236–245. ACM, New York (2004)

    Chapter  Google Scholar 

  13. Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: Proceedings of the 18th International Conference on Data Engineering (ICDE 2002), pp. 673–684 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Dan Halperin Kurt Mehlhorn

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Djordjevic, B., Gudmundsson, J., Pham, A., Wolle, T. (2008). Detecting Regular Visit Patterns. In: Halperin, D., Mehlhorn, K. (eds) Algorithms - ESA 2008. ESA 2008. Lecture Notes in Computer Science, vol 5193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87744-8_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87744-8_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87743-1

  • Online ISBN: 978-3-540-87744-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics