Skip to main content
Springer Nature Link
Log in

PNN query processing on compressed trajectories

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

Trajectory compression is widely used in spatial-temporal databases as it can notably reduce (i) the computation/communication load of clients (GPS-enabled mobile devices) and (ii) the storage cost of servers. Compared with original trajectories, compressed trajectories have clear advantages in data processing, transmitting, storing, etc. In this paper, we investigate a novel problem of searching the Path Nearest Neighbor based on Compressed Trajectories (PNN-CT query). This type of query is conducted on compressed trajectories and the target is to retrieve the PNN with the highest probability (lossy compression leads to the uncertainty), which can bring significant benefits to users in many popular applications such as trip planning. To answer the PNN-CT query effectively and efficiently, a two-phase solution is proposed. First, we use the meta-data and sample points to specify a tight search range. The key of this phase is that the number of data objects/trajectory segments to be processed or decompressed should be kept as small as possible. Our efficiency study reveals that the candidate sets created are tight. Second, we propose a reconstruction algorithm based on probabilistic models to account for the uncertainty when decompressing the trajectory segments in the candidate set. Furthermore, an effective combination strategy is adopted to find the PNN with the highest probability. The complexity analysis shows that our solution has strong advantages over existing methods. The efficiency of the proposed PNN-CT query processing is verified by extensive experiments based on real and synthetic trajectory data in road networks.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. Bikely (http://www.bikely.com/) GPS-Waypoints (http://www.gps-waypoints.net/) Share My Routes (http://www.sharemyroutes.com/) Microsoft GeoLife (http://research.microsoft.com/en-us/projects/geolife/).

  2. http://www.iscas.ac.cn/

  3. www.cs.fsu.edu/∼lifeifei/SpatialDataset.htm

References

  1. Aggarwal CC, Agrawal D (2003) On nearest neighbor indexing of nonlinear trajectories. In: PODS, pp 252–259

  2. Alt H, Efrat A, Rote G, Wenk C (2003) Matching planar maps. In: SODA, pp 589–598

  3. Bellman R, Dreyfus S (1962) Applied dynamic programming. Princeton University Press, Princeton

    Google Scholar 

  4. Bellman RE (1961) On the approximation of curves by line segments using dynamic programming. CACM 4(6):284

    Google Scholar 

  5. Bhattacharya A, Das SK (1999) Lezi-update: an information-theoretic approach to track mobile users in pcs networks. In: MobiCom, pp 1–12

  6. Brakatsoulas S, Pfoser D, Salas R, Wenk C (2005) On map-matching vehicle tracking data. In: VLDB, pp 853–864

  7. Cao H, Wolfson O, Trajcevski G (2006) Spatio-temporal data reduction with deterministic error bounds. In: VLDB J, vol 15, pp 211–228

  8. Chen Z, Shen HT, Zhou X, Yu JX (2009) Monitoring path nearest neighbor in road networks. In: SIGMOD, pp 591–602

  9. Dijkstra EW (1959) A note on two problems in connection with graphs. Numer Math 1:269–271

    Article  Google Scholar 

  10. Douglas D, Peucker T (1973) Algorithms for the reduction of the number of points required to represent a line or its caricature. In: The Canadian cartographer, vol 10, pp 112–122

  11. Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: SIGKDD, pp 330–339

  12. Greenfeld J (2002) Matching gps observations to locations on a digital map. In: 81th annual meeting of the transportation research board

  13. Hjaltason GR, Samet H (1999) Distance browsing in spatial databases. ACM TODS 24(2):265–318

    Article  Google Scholar 

  14. Jensen CS, Lin D, Ooi BC (2004) Query and update efficient b+-tree based indexing of moving objects. In: VLDB, pp 768–779

  15. Jeung H, Liu Q, Shen HT, Zhou X (2008) A hybrid prediction model for moving objects. In: ICDE, pp 70–79

  16. Kleinberg J, Tardos E (2005) Algorithm design. Addison-Wesley, Reading, MA

  17. Lange R, Farrell T, Drr F, Rothermel K (2009) Remote real-time trajectory simplification. In: PerCom, pp 1–10

  18. Liu K, Deng K, Ding Z, Li M, Zhou X (2009) Moir/mt: monitoring large-scale road network traffic in real-time. In: VLDB, pp 1538–1541

  19. Meratnia N, By RAd (2004) Spatiotemporal compression techniques for moving point objects. In: EDBT, pp 765–782

  20. Muckell J, Hwang J-H, Lawson C, Ravi S (2010) Algorithms for compressing gps trajectory data: an empirical evaluation. In: ACM GIS

  21. Patel JM, Chen Y, Chakka VP (2004) Stripes: an efficient index for predicted trajectories. In: SIGMOD, pp 635–646

  22. Pei J, Hua M, Tao Y, Lin X (2008) Query answering techniques on uncertain and probabilistic data: tutorial summary. In: SIGMOD

  23. Rabiner L (1989) A tutorial on hidden markov models and selected applications in speech recognition. In: IEEE Proceedings, vol 77, pp 257–286

  24. Roussopoulos N, Kelley S, Vincent F (1995) Nearest neighbor queries. In: SIGMOD, pp 71–79

  25. Saltenis S, Jensen CS, Leutenegger ST, Lopez MA (2000) Indexing the positions of continuously moving objects. In: SIGMOD, pp 331–342

  26. Shekhar S, Yoo JS (2003) Processing in-route nearest neighbor queries: a comparison of alternative approaches. In: ACM GIS, pp 9–16

  27. Suciu D, Dalvi N (2005) Foundations of probabilistic answers to queries. In: SIGMOD tutorial

  28. Tao Y, Faloutsos C, Papadias D, Liu B (2004) Prediction and indexing of moving objects with unknown motion patterns. In: SIGMOD

  29. Tao Y, Kollios G, Considine J, Li F, Papadias D (2004) Spatio-temporal aggregation using sketches. In: ICDE, p 214

  30. Tao Y, Papadias D (2003) Spatial queries in dynamic environments. ACM TODS 28(2):101–139

    Article  Google Scholar 

  31. Tao Y, Papadias D, Sun J (2003) The tpr*-tree: an optimized spatiotemporal access method for predictive queries. In: VLDB, pp 790–801

  32. Tao Y, Xiao X, Cheng R (2007) Range search on multidimensional uncertain data. ACM TODS 32(3):15–54

    Article  Google Scholar 

  33. Trajcevski G, Tamassia R, Ding H, Scheuermann P, Cruz IF (2009) Continuous probabilistic nearest-neighbor queries for uncertain trajectories. In: EDBT, pp 874–885

  34. Trajcevski G, Wolfson O, Hinrichs K, Chamberlain S (2004) Managing uncertainty in moving objects databases. ACM TODS 29(3):463–507

    Article  Google Scholar 

  35. Wenk C, Salas R, Pfoser D (2006) Addressing the need for map-matching speed: localizing globalb curve-matching algorithms. In: SSDBM

  36. Yoo JS, Shekhar S (2005) In-route nearest neighbor queries. GeoInformatica 9:117–137

    Article  Google Scholar 

  37. Zheng K, Trajcevski G, Zhou X, Scheuermann P (2011) Probabilistic range queries for uncertain trajectories on road networks. In: EDBT

Download references

Acknowledgements

We wish to thank the anonymous reviewers for several comments and suggestions that have improved the paper. This work was supported by the ARC grant DP110103423 and the National Natural Science Foundation of China (No.60905030).

Author information

Authors and Affiliations

  1. School of Information Technology & Electrical Engineering, The University of Queensland, Brisbane St. Lucia, QLD, 4072, Australia

    Shuo Shang, Ke Deng, Kexin Xie, Kai Zheng & Xiaofang Zhou

  2. Division of Informatics, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, People’s Republic of China

    Bo Yuan

Authors
  1. Shuo Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ke Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kexin Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kai Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaofang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuo Shang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shang, S., Yuan, B., Deng, K. et al. PNN query processing on compressed trajectories. Geoinformatica 16, 467–496 (2012). https://doi.org/10.1007/s10707-011-0144-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-011-0144-5

Keywords