Processing (Multiple) Spatio-temporal Range Queries in Multicore Settings

  • Goce Trajcevski
  • Anan Yaagoub
  • Peter Scheuermann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6909)


Research in Moving Objects Databases (MOD) has addressed various aspects of storing and querying trajectories of moving objects: from modelling, through linguistic constructs and formalisms/ algebras, to indexing structures and efficient processing of different query-categories have been subjects to a large body of works. Given the architectural trends of multicore CPUs becoming a commonplace, in this work we focus on efficient processing of spatio-temporal range queries in such settings. We postulate that coupling the semantics of the problem domain into the query processing algorithms in a manner that is aware of the multicore features, can yield performance improvements that surpass the gains obtained by relying solely on the compiler-generated threads parallelization. Towards that end, we present and evaluate heuristics for processing variants spatio-temporal range queries in multicore settings by partitioning the load (i.e., data set) and assigning partial tasks to the individual cores. Our experiments demonstrate that 5-fold speed-ups can be achieved, when compared to the (semi) naive approach which relies on the compiler to generate the multicore-compatible code.


Query Processing Range Query Continuous Query Multicore Setting Query Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abbott, T.G., Burr, M., Chan, T.M., Demaine, E.D., Demaine, M.L., Hugg, J., Kane, D.M., Langerman, S., Nelson, J., Rafalin, E., Seyboth, K., Yeung, V.: Dynamic ham-sandwich cuts in the plane. Comput. Geom. 42(5), 419–428 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Barroso, L.A., Gharachorloo, K., McNamara, R., Nowatzyk, A., Qadeer, S., Sano, B., Smith, S., Stets, R., Verghese, B.: Piranha: a scalable architecture based on single-chip multiprocessing. In: Proceedings of the 27th Annual International Symposium on Computer Architecture, pp. 282–293 (2000)Google Scholar
  3. 3.
    Benetis, R., Jensen, C.S., Karciauskas, G., Saltenis, S.: Nearest and reverse nearest neighbor queries for moving objects. VLDB J. 15(3), 229–249 (2006)CrossRefGoogle Scholar
  4. 4.
    Bestehorn, M., Böhm, K., Bradley, P., Buchmann, E.: Deriving spatio-temporal query results in sensor networks. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 6–23. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Blazewicz, J., Ecker, K., Plateau, B. (eds.): Handbook on parallel and distributed processing. Springer, Heidelberg (2000)zbMATHGoogle Scholar
  6. 6.
    Camp, T., Boleng, J., Davies, V.: A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing 2(5) (2002)Google Scholar
  7. 7.
    Cheng, R., Kalashnikov, D., Prabhakar, S.: Evaluating probabilistic queries over imprecise data. In: SIGMOD (2003)Google Scholar
  8. 8.
    Coman, A., Nascimento, M.A., Sander, J.: A framework for spatio-temporal query processing over wireless sensor networks. In: DMSN, pp. 104–110 (2004)Google Scholar
  9. 9.
    Demiryurek, U., Pan, B., Kashani, F.B., Shahabi, C.: Towards modeling the traffic data on road networks. In: GIS-IWCTS (2009)Google Scholar
  10. 10.
    Ding, H., Trajcevski, G., Scheuermann, P.: Towards efficient maintenance of continuous queries for trajcectories. GeoInformatica 12(3) (2008)Google Scholar
  11. 11.
    du Mouza, C., Rigaux, R.: Multi-scale classification of moving objects trajectories. In: SSDBM (2004)Google Scholar
  12. 12.
    Gedik, B., Liu, L.: Mobieyes: A distributed location monitoring service using moving location queries. IEEE Transactions on Mobile Computing 5(10) (2006)Google Scholar
  13. 13.
    Gedik, B., Liu, L.: Quality-aware distributed data delivery for continuous query services. In: SIGMOD Conference (2006)Google Scholar
  14. 14.
    George, B., Kim, S., Shekhar, S.: Spatio-temporal network databases and routing algorithms: A summary of results. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 460–477. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Ghandeharizadeh, S., DeWitt, D.J.: Hybrid-range partitioning strategy: A new declustering strategy for multiprocessor database machines. In: McLeod, D., Sacks-Davis, R., Schek, H.-J. (eds.) 16th International Conference on Very Large Data Bases, pp. 481–492 (1990)Google Scholar
  16. 16.
    Güting, R.H., de Almeida, V.T., Ding, Z.: Modeling and querying moving objects in networks. VLDB J. 15(2) (2006)Google Scholar
  17. 17.
    Güting, R.H., Schneider, M.: Moving Objects Databases. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  18. 18.
    Hadjieleftheriou, M., Kollios, G., Tsotras, V.J., Gunopulos, D.: Efficient indexing of spatiotemporal objects. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Hwang, J., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, p. 251. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  19. 19.
    Hadjieleftheriou, M., Kriakov, V., Tao, Y., Kollios, G., Delis, A., Tsotras, V.J.: Spatio-temporal data services in a shared-nothing environment. In: SSDBM, pp. 131–134 (2004)Google Scholar
  20. 20.
    Herlihy, M., Shavit, N.: The Art of Multiprocessor Programming. Morgan Kaufmann, San Francisco (2008)Google Scholar
  21. 21.
    Iwerks, G.S., Samet, H., Smith, K.P.: Maintenance of k-nn and spatial join queries on continuously moving points. ACM Transactions on Database Systems (TODS) 31(2) (2006)Google Scholar
  22. 22.
    Kongetira, P., Aingaran, K., Olukotun, K.: Niagara: A 32-way multithreaded sparc processor. IEEE Micro. 25, 21–29 (2005)CrossRefGoogle Scholar
  23. 23.
    Koubarakis, M., Sellis, T., Frank, A.U., Grumbach, S., Güting, R.H., Jensen, C.S., Lorentzos, N., Manolopoulos, Y., Nardelli, E., Pernici, B., Scheck, H.-J., Scholl, M., Theodoulidis, B., Tryfona, N.: Spatio-Temporal Databases – the CHOROCHRONOS Approach. LNCS, vol. 2520. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  24. 24.
    Kujipers, B., Othman, W.: Trajectory databases: data models, uncertainty and complete query languages. Journal of Computer and System Sciences (2009), doi:10.1016/j.jcss.2009.10.002Google Scholar
  25. 25.
    Lema, J.A.C., Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: Algorithms for moving objects databases. Computing Journal 46(6) (2003)Google Scholar
  26. 26.
    Lin, D., Cui, B., Yang, D.: Optimizing moving queries over moving object data streams. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 563–575. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. 27.
    Mokbel, M.F., Aref, W.G.: SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams. VLDB J. 17(5), 971–995 (2008)CrossRefGoogle Scholar
  28. 28.
    Mokhtar, H., Su, J.: Questo: A query language for uncertain and exact spatio-temporal objects. In: Atzeni, P., Caplinskas, A., Jaakkola, H. (eds.) ADBIS 2008. LNCS, vol. 5207, pp. 184–198. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  29. 29.
    Mouratidis, K., Yiu, M.L., Papadias, D., Mamoulis, N.: Continuous nearest neighbor monitoring in road networks. In: VLDB, pp. 43–54 (2006)Google Scholar
  30. 30.
    Olukotun, K., Nayfeh, B.A., Hammond, L., Wilson, K., Chang, K.: The case for a single-chip multiprocessor. In: ASPLOS, pp. 2–11 (1996)Google Scholar
  31. 31.
    O’Rourke, J.: Computational Geometry in C. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  32. 32.
    Pelanis, M., Saltenis, S., Jensen, C.S.: Indexing the past, present, and anticipated future positions of moving objects. ACM TODS 31(1) (2006)Google Scholar
  33. 33.
    Schiller, J.H., Voisard, A. (eds.): Location-Based Services. Morgan Kaufmann, San Francisco (2004)Google Scholar
  34. 34.
    Shermer, T.C.: A linear algorithm for bisecting a polygon. Inf. Process. Lett. 41(3), 135–140 (1992)CrossRefzbMATHGoogle Scholar
  35. 35.
    Stojmenovic, I.: Bisections and ham-sandwich cuts of convex polygons and polyhedra. Inf. Process. Lett. 38(1), 15–21 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Tao, Y., Papadias, D., Sun, J.: The tpr∗-tree: An optimized spatio-temporal access method for predictive queries. In: VLDB (2003)Google Scholar
  37. 37.
    Trajcevski, G., Wolfson, O., Hinrichs, K., Chamberlain, S.: Managing uncertainty in moving objects databases. ACM TODS 29(3) (2004)Google Scholar
  38. 38.
    Yoon, H., Shahabi, C.: Robust time-referenced segmentation of moving object trajectories. In: ICDM (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Goce Trajcevski
    • 1
  • Anan Yaagoub
    • 1
  • Peter Scheuermann
    • 1
  1. 1.Dept. of Electrical Engineering and Computer ScienceNorthwestern UniversityEvanstonUSA

Personalised recommendations