Skip to main content
SpringerLink
Log in

Efficient moving k nearest neighbor queries over line segment objects

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

The growing need for location based services motivates the moving k nearest neighbor query (MkNN), which requires to find the k nearest neighbors of a moving query point continuously. In most existing solutions, data objects are abstracted as points. However, lots of real-world data objects, such as roads, rivers or pipelines, should be reasonably modeled as line segments or polyline segments. In this paper, we present LV*-Diagram to handle MkNN queries over line segment data objects. LV*-Diagram dynamically constructs a safe region. The query results remain unchanged if the query point is in the safe region, and hence, the computation cost of the server is greatly reduced. Experimental results show that our approach significantly outperforms the baseline method w.r.t. CPU load, I/O, and communication costs.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21

Similar content being viewed by others

Notes

  1. Fixed-rank means the rank of an object doesn’t change. It is specifically decorate a region (i.e. fixed-ranked region) in our paper, which indicates the ranks of all the objects in such region keep fixed.

  2. Ranking is used to represent the sequence with the objects sorted in this paper. For example, we can say the ranking (of s 1, s 2 and s 3) is 〈s 1, s 3, s 2〉.

  3. The rank of an object means the object’s position in a list of objects sorted by their distances to some other objects.

  4. http://www.chorochronos.org/?q=node/54

References

  1. Ali, M.E., Tanin, E., Zhang, R., Kotagiri, R.: Probabilistic voronoi diagrams for probabilistic moving nearest neighbor queries. Data Knowl. Eng. 75, 1–33 (2012)

    Article  Google Scholar 

  2. Aly, A.M., Aref, W.G., Ouzzani, M.: Spatial queries with two knn predicates. Proc. VLDB Endowment 5(11), 1100C1111 (2012)

    Article  Google Scholar 

  3. Beckmann, N., Kriegel, H., Schneider, R., Seeger, B.: The r*-tree: an efficient and robust access method for points and rectangles. SIGMOD Record 19(2), 322–331 (1990)

    Article  Google Scholar 

  4. Bespamyatnikha, S., Snoeyinka, J.: Queries with segments in Voronoi diagrams. Comput. Geom. 16(1), 23–33 (2000)

    Article  MathSciNet  Google Scholar 

  5. Gao, Y., Zheng, B.: Continuous obstructed nearest neighbor queries in spatial databases. In: Proceedings of SIGMOD, pp. 577–590 (2009)

  6. Gao, Y., Zheng, B., Lee, W.C., Chen, G.: Continuous visible nearest neighbor queries. In: Proceedings of 14th International Conference on Extending Database Technology, pp. 144–155 (2009)

  7. Gao, Y., Zheng, B., Chen, G., Chen, C., Li, Q.: Continuous nearest-neighbor search in the presence of obstacles. ACM Trans. Database Syst. 36(2) (2011)

  8. Gao, Y., Zheng, B., Chen, G., Li, Q., Guo, X.: Continuous visible nearest neighbor query processing in spatial databases. VLDB J. 20(3), 371–396 (2011)

    Article  Google Scholar 

  9. Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proceedings of SIGMOD, pp. 47–57 (1984)

  10. Held, M.: VRONI: An engineering approach to the reliable and efficient computation of Voronoi diagrams of points and line segments. Comput. Geom. 18(2), 95–123 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  11. Hjaltason, G.R., Samet, H.: Ranking in spatial databases. In: Symposium on Large Spatial Databases, pp. 83–95 (1995)

  12. Hu, H., Xu, J., Lee, D.L.: A generic framework for monitoring continuous spatial queries over moving objects. In: Processings of SIGMOD, pp. 479C490 (2005)

  13. Kolahdouzan, M., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: Proceedings of VLDB, pp. 840–851 (2004)

  14. Kulik, L., Tanin, E.: Incremental rank updates for moving query points. In: Proceedings of GIScience, pp. 251–268 (2006)

  15. Kwon, H., Whang, K., Song, I., Wang, H.: RASIM: a rank-aware separate index method for answering top-k spatial keyword queries. World Wide Web 16(2), 111–139 (2013)

    Article  Google Scholar 

  16. Li, Y., Yang, J., Han, J.: Continuous k-nearest neighbor search for moving objects. In: Proceedings of SSDBM, pp. 631–642 (2004)

  17. Li, C., Gu, Y., Li, F., Chen, M.: Moving K-nearest neighbor query over ob-structed regions. In: Proceedings of the 12th International Asia-Pacific Web Con-ference, pp. 29–35 (2010)

  18. Nutanong, S., Zhang, R., Tanin, E., Kulik, L.: The V*diagram: A query dependent approach to moving knn queries. In: Proceedings of VLDB, pp. 1095–1106 (2008)

  19. Nutanong, S., Zhang, R., Tanin, E., Kulik, L.: Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries. VLDB J. 19(3), 307–332 (2010)

    Article  Google Scholar 

  20. Okabe, A., Boots, B., Sugihara, K., Chiu, S.N.: Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. Wiley (1992)

  21. Qi, J., Zhang, R., Wang, Y., Xue, A., Yu, G., Kulik, L.: The min-dist location selection and facility replacement queries. World Wide Web 17(6), 1261–1293 (2014)

    Article  Google Scholar 

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

  23. Song, Z., Roussopoulos, N.: K-nearest neighbor search for moving query point. In: Processings of SSTD, pp. 79C96 (2001)

  24. Tao, Y., Papadias, D.: Time-parameterized queries in spatio-temporal databases. In: Proceedings of SIGMOD, pp. 334–345 (2002)

  25. Tao, Y., Papadias, D., Shen, Q.: Continuous nearest neighbor search. In: Proceedings of VLDB, pp. 287–298 (2002)

  26. Tao, Y., Zhang, J., et al.: An efficient cost model for optimization of nearest neighbor search in low and medium dimensional spaces. IEEE Trans. Knowl. Data Eng. 16(10), 1169–118 (2004)

    Article  Google Scholar 

  27. Zhang, J., Zhu, M., Papadias, D., Tao, Y., Lee, D.L.: Location-based spatial queries. In: Proceedings of SIGMOD, pp. 443–454 (2003)

Download references

Acknowledgments

This work is supported by the National Basic Research Program of China under Grant No.2012CB316201,the National Natural Science Foundation of China under Grant No.61472071 and 61003058,and the Fundamental Research Funds for the Central Universities of China under Grant No. N130404010.

Author information

Authors and Affiliations

  1. Northeastern University, Shenyang, People’s Republic of China

    Yu Gu, Hui Zhang, Zhigang Wang & Ge Yu

Authors
  1. Yu Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhigang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ge Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu Gu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gu, Y., Zhang, H., Wang, Z. et al. Efficient moving k nearest neighbor queries over line segment objects. World Wide Web 19, 653–677 (2016). https://doi.org/10.1007/s11280-015-0351-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-015-0351-3

Keywords

Search

Navigation