Skip to main content

Geometric Spanners in the MapReduce Model

  • Conference paper
  • First Online:
Computing and Combinatorics (COCOON 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10976))

Included in the following conference series:

Abstract

A geometric spanner on a point set is a sparse graph that approximates the Euclidean distances between all pairs of points in the point set. Here, we intend to construct a geometric spanner for a massive point set, using a distributed algorithm on parallel machines. In particular, we use the MapReduce model of computation to construct spanners in several rounds with inter-communications in between. An algorithm in this model is called efficient if it uses a sublinear number of machines and runs in a polylogarithmic number of rounds. In this paper, we propose an efficient MapReduce algorithm for constructing a geometric spanner in a constant number of rounds, using linear amount of communication. The stretch factors of our spanner is \(1+\epsilon \), for any \(\epsilon >0\).

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

Access this chapter

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

Institutional subscriptions

References

  1. Agarwal, P.K., Fox, K., Munagala, K., Nath, A.: Parallel algorithms for constructing range and nearest-neighbor searching data structures. In: Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 429–440. ACM (2016)

    Google Scholar 

  2. Agarwal, P.K., Har-Peled, S., Varadarajan, K.R.: Geometric approximation via coresets. Comb. Comput. Geom. 52, 1–30 (2005)

    MathSciNet  MATH  Google Scholar 

  3. Andoni, A., Nikolov, A., Onak, K., Yaroslavtsev, G.: Parallel algorithms for geometric graph problems. In: Proceedings of the 46th Annual ACM Symposium on Theory of Computing, pp. 574–583 (2014)

    Google Scholar 

  4. Bakhshesh, D., Farshi, M.: Geometric spanners merging and its applications. In: Proceedings of the 28th Canadian Conference on Computational Geometry, pp. 133–139 (2016)

    Google Scholar 

  5. Birn, M., Osipov, V., Sanders, P., Schulz, C., Sitchinava, N.: Efficient parallel and external matching. In: Wolf, F., Mohr, B., an Mey, D. (eds.) Euro-Par 2013. LNCS, vol. 8097, pp. 659–670. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40047-6_66

    Chapter  Google Scholar 

  6. Callahan, P.B.: Dealing with higher dimensions: the well-separated pair decomposition and its applications. Ph.D. thesis, Johns Hopkins University (1995)

    Google Scholar 

  7. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)

    Article  Google Scholar 

  8. Eldawy, A., Li, Y., Mokbel, M.F., Janardan, R.: CG\(\_\)Hadoop: computational geometry in MapReduce. In: Proceedings 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 294–303 (2013)

    Google Scholar 

  9. Eldawy, A., Mokbel, M.F.: Communication steps for parallel query processing. In: Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 273–284 (2013)

    Google Scholar 

  10. Eldawy, A., Mokbel, M.F.: SpatialHadoop: A MapReduce framework for spatial data. In: Proceedings of the 31st International Conference on Data Engineering, pp. 1352–1363 (2015)

    Google Scholar 

  11. Farley, A.M., Proskurowski, A., Zappala, D., Windisch, K.: Spanners and message distribution in networks. Discrete Appl. Math. 137, 159–171 (2004)

    Article  MathSciNet  Google Scholar 

  12. Ghodsi, M., Sack, J.: A coarse grained solution to parallel terrain simplification. In: Proceedings of 10th Canadian Conference on Computational Geometry (1998)

    Google Scholar 

  13. Goodrich, M.T.: Simulating parallel algorithms in the MapReduce framework with applications to parallel computational geometry. arXiv preprint arXiv:1004.4708 (2010)

  14. Goodrich, M.T., Sitchinava, N., Zhang, Q.: Sorting, searching, and simulation in the MapReduce framework. In: Proceedings of the 22nd Annual International Symposium on Algorithms and Computation, pp. 374–383 (2011)

    Chapter  Google Scholar 

  15. Im, S., Moseley, B., Sun, X.: Efficient massively parallel methods for dynamic programming. In: Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, pp. 798–811. ACM (2017)

    Google Scholar 

  16. Karloff, H., Suri, S., Vassilvitskii, S.: A model of computation for MapReduce. In: Proceedings of the 21st ACM-SIAM Symposium on Discrete Algorithms, pp. 938–948 (2010)

    Chapter  Google Scholar 

  17. Narasimhan, G., Smid, M.: Geometric Spanner Networks. Cambridge University Press, Cambridge (2007)

    Book  Google Scholar 

  18. Nath, A., Fox, K., Munagala, K., Agarwal, P.K.: Massively parallel algorithms for computing TIN DEMs and contour trees for large terrains. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2016)

    Google Scholar 

  19. Navarro, G., Paredes, R., Chávez, E.: t-spanners as a data structure for metric space searching. In: Laender, A.H.F., Oliveira, A.L. (eds.) SPIRE 2002. LNCS, vol. 2476, pp. 298–309. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45735-6_26

    Chapter  Google Scholar 

  20. Rao, S.B., Smith, W.D.: Approximating geometrical graphs via “spanners” and “banyans”. In: Proceedings of the 30th Annual ACM Symposium on Theory of Computing, pp. 540–550 (1998)

    Google Scholar 

  21. van Kreveld, M.: Algorithms for triangulated terrains. In: Plášil, F., Jeffery, K.G. (eds.) SOFSEM 1997. LNCS, vol. 1338, pp. 19–36. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63774-5_95

    Chapter  Google Scholar 

  22. Xia, G.: The stretch factor of the Delaunay triangulation is less than 1.998. SIAM J. Comput. 42, 1620–1659 (2013)

    Article  MathSciNet  Google Scholar 

  23. Yaroslavtsev, G., Vadapalli, A.: Massively parallel algorithms and hardness for single-linkage clustering under \(\ell_p\)-distances. arXiv preprint arXiv:1710.01431 (2017)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sepideh Aghamolaei .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aghamolaei, S., Baharifard, F., Ghodsi, M. (2018). Geometric Spanners in the MapReduce Model. In: Wang, L., Zhu, D. (eds) Computing and Combinatorics. COCOON 2018. Lecture Notes in Computer Science(), vol 10976. Springer, Cham. https://doi.org/10.1007/978-3-319-94776-1_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-94776-1_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94775-4

  • Online ISBN: 978-3-319-94776-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics