Advertisement

The VLDB Journal

, Volume 21, Issue 4, pp 561–586 | Cite as

A highly optimized algorithm for continuous intersection join queries over moving objects

  • Rui ZhangEmail author
  • Jianzhong Qi
  • Dan Lin
  • Wei Wang
  • Raymond Chi-Wing Wong
Regular Paper

Abstract

Given two sets of moving objects with nonzero extents, the continuous intersection join query reports every pair of intersecting objects, one from each of the two moving object sets, for every timestamp. This type of queries is important for a number of applications, e.g., in the multi-billion dollar computer game industry, massively multiplayer online games like World of Warcraft need to monitor the intersection among players’ attack ranges and render players’ interaction in real time. The computational cost of a straightforward algorithm or an algorithm adapted from another query type is prohibitive, and answering the query in real time poses a great challenge. Those algorithms compute the query answer for either too long or too short a time interval, which results in either a very large computation cost per answer update or too frequent answer updates, respectively. This observation motivates us to optimize the query processing in the time dimension. In this study, we achieve this optimization by introducing the new concept of time-constrained (TC) processing. Further, TC processing enables a set of effective improvement techniques on traditional intersection join algorithms. Finally, we provide a method to find the optimal value for an important parameter required in our technique, the maximum update interval. As a result, we achieve a highly optimized algorithm for processing continuous intersection join queries on moving objects. With a thorough experimental study, we show that our algorithm outperforms the best adapted existing solution by several orders of magnitude. We also validate the accuracy of our cost model and its effectiveness in optimizing the performance.

Keywords

Spatial databases Moving objects Continuous intersection join 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agarwal, P.K., Arge, L., Erickson, J.: Indexing moving points. In: Proceedings of PODS, pp. 175–186 (2000)Google Scholar
  2. 2.
    Ali, M.E., Zhang, R., Tanin, E., Kulik, L.: A motion-aware approach to continuous retrieval of 3d objects. In: Proceedings of ICDE, pp. 843–852 (2008)Google Scholar
  3. 3.
    Arumugam, S., Jermaine, C.: Closest-point-of-approach join for moving object histories. In: Proceedings of ICDE, p. 86 (2006)Google Scholar
  4. 4.
    Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. In: Proceedings of SIGMOD (1990)Google Scholar
  5. 5.
    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
  6. 6.
    Bially T.: Space-filling curves: their generation and their application to bandwidth reduction. IEEE Trans. Inf. Theory 15, 658–664 (1969)CrossRefGoogle Scholar
  7. 7.
    Botea V., Mallett D., Nascimento M., Sander J.: Pist: an efficient and practical indexing technique for historical spatio-temporal point data. GeoInformatica 12, 143–168 (2008)CrossRefGoogle Scholar
  8. 8.
    Brinkhoff, T., Kriegel, H.-P., Seeger, B.: Efficient processing of spatial joins using r-trees. In: Proceedings of SIGMOD, pp. 237–246 (1993)Google Scholar
  9. 9.
    Civilis, A., Jensen, C.S., Nenortaite, J., Pakalnis, S.: Efficient tracking of moving objects with precision guarantees. In: Proceedings of MobiQuitous, pp. 164–173 (2004)Google Scholar
  10. 10.
    Dahmann, J.S., Fujimoto, R., Weatherly, R.M.: The department of defense high level architecture. In: Proceedings of Winter Simulation Conference, pp. 142–149 (1997)Google Scholar
  11. 11.
    Demers, A.J., Gehrke, J., Koch, C., Sowell, B., White, W.M.: Database research in computer games. In: Proceedings of SIGMOD, pp. 1011–1014 (2009)Google Scholar
  12. 12.
    Ding, H., Trajcevski, G., Scheuermann, P.: Omcat: optimal maintenance of continuous queries’ answers for trajectories. In: Proceedings of SIGMOD, pp. 748–750 (2006)Google Scholar
  13. 13.
    Frentzos, E., Gratsias, K., Pelekis, N., Theodoridis, Y.: Nearest neighbor search on moving object trajectories. In: Proceedings of SSTD, pp. 328–345 (2005)Google Scholar
  14. 14.
    Güting R.H., Behr T., Xu J.: Efficient k-nearest neighbor search on moving object trajectories. VLDB J. 19(5), 687–714 (2010)CrossRefGoogle Scholar
  15. 15.
    Iwerks, G.S., Samet, H., Smith, K.P.: Continuous k-nearest neighbor queries for continuously moving points with updates. In: Proceedings of VLDB, pp. 512–523 (2003)Google Scholar
  16. 16.
    Iwerks, G.S., Samet, H., Smith, K.P.: Maintenance of spatial semijoin queries on moving points. In: Proceedings of VLDB, pp. 828–839 (2004)Google Scholar
  17. 17.
    Iwerks G.S., Samet H., Smith K.P.: Maintenance of k-nn and spatial join queries on continuously moving points. Trans. Database Syst. 31(2), 485–536 (2006)CrossRefGoogle Scholar
  18. 18.
    Jensen, C., Lin, D., Ooi, B.C.: Query and update efficient B+-tree based indexing of moving objects. In: Proceedings of VLDB, pp. 768–779 (2004)Google Scholar
  19. 19.
    Jeung H., Yiu M.L., Zhou X., Jensen C.S., Shen H.T.: Discovery of convoys in trajectory databases. Proc. VLDB Endow. 1(1), 1068–1080 (2008)Google Scholar
  20. 20.
    Kollios, G., Gunopulos, D., Tsotras, V.J.: On indexing mobile objects. In: Proceedings of PODS, pp. 261–272 (1999)Google Scholar
  21. 21.
    Kollios G., Tsotras V.J., Gunopulos D., Delis A., Hadjieleftheriou M.: Indexing animated objects using spatiotemporal access methods. TKDE 13(5), 758–777 (2001)Google Scholar
  22. 22.
    Koudas, N., Sevcik, K.C.: Size separation spatial join. In: Proceedings of SIGMOD, pp. 324–335 (1997)Google Scholar
  23. 23.
    Lo, M.-L., Ravishankar, C.V.: Spatial joins using seeded trees. In: Proceedings of SIGMOD, pp. 209–220 (1994)Google Scholar
  24. 24.
    Mokbel, M.F., Xiong, X., Aref, W.G.: Sina: scalable incremental processing of continuous queries in spatio-temporal databases. In: Proceedings of SIGMOD, pp. 623–634 (2004)Google Scholar
  25. 25.
    Morse, K.L.: Interest management in large-scale distributed simulations. Technical Report ICS-TR-96-27 (1996)Google Scholar
  26. 26.
    Mouratidis, K., Hadjieleftheriou, M., Papadias, D.: Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: Proceedings of SIGMOD, pp. 634–645 (2005)Google Scholar
  27. 27.
    Nascimento, M.A., Silva, J.R.O.: Towards historical R-trees. In: Proceedings of SAC, pp. 235–240 (1998)Google Scholar
  28. 28.
    Nutanong, S., Tanin, E., Shao, J., Zhang, R., Ramamohanarao, K.: Continuous detour queries in spatial networks. TKDE (to appear)Google Scholar
  29. 29.
    Nutanong S., Zhang R., Tanin E., Kulik L.: The V*-diagram: a query-dependent approach to moving knn queries. Proc. VLDB Endow. 1(1), 1095–1106 (2008)Google Scholar
  30. 30.
    Nutanong S., Zhang R., Tanin E., Kulik L.: Analysis and evaluation of V*-kNN: an efficient algorithm for moving knn queries. VLDB J. 19, 307–332 (2010)CrossRefGoogle Scholar
  31. 31.
    Orenstein, J.: Spatial query processing in an object-oriented database system. In: Proceedings of SIGMOD, pp. 326–336 (1986)Google Scholar
  32. 32.
    Patel, J.M., Chen, Y., Chakka, V.P.: STRIPES: an efficient index for predicted trajectories. In: Proceedings of SIGMOD, pp. 637–646 (2004)Google Scholar
  33. 33.
    Patel, J.M., DeWitt, D.J.: Partition based spatial-merge join. In: Proceedings of SIGMOD, pp. 259–270 (1996)Google Scholar
  34. 34.
    Pfoser, D., Jensen, C.S., Theodoridis, Y.: Novel approaches in query processing for moving object trajectories. In: Proceedings of VLDB (2000)Google Scholar
  35. 35.
    Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: Proceedings of SIGMOD, pp. 331–342 (2000)Google Scholar
  36. 36.
    Sevcik, K.C., Koudas, N.: Filter trees for managing spatial data over a range of size granularities. In: Proceedings of VLDB, pp. 16–27 (1996)Google Scholar
  37. 37.
    Sistla, A.P., Wolfson, O., Chamberlain, S., Dao, S.: Modeling and querying moving objects. In: Proceedings of ICDE, pp. 422–432 (1997)Google Scholar
  38. 38.
    Tao, Y., Papadias, D.: Mv3r-tree: a spatio-temporal access method for timestamp and interval queries. In: Proceedings of VLDB, pp. 431–440 (2001)Google Scholar
  39. 39.
    Tao, Y., Papadias, D.: Time-parameterized queries in spatio-temporal databases. In: Proceedings of SIGMOD, pp. 334–345 (2002)Google Scholar
  40. 40.
    Tao, Y., Papadias, D., Sun, J.: The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: Proceedings of VLDB, pp. 790–801 (2003)Google Scholar
  41. 41.
    Leong Hou U., Mamoulis N., Yiu M.L.: Computation and monitoring of exclusive closest pairs. Trans. Knowl. Data Eng. 20(12), 1641–1654 (2008)CrossRefGoogle Scholar
  42. 42.
    White W., Koch C., Gupta N., Gehrke J., Demers A.: Database research opportunities in computer games. SIGMOD Rec. 36(3), 7–13 (2007)CrossRefGoogle Scholar
  43. 43.
    Yiu M., Tao Y., Mamoulis N.: The Bdual-tree: indexing moving objects by space filling curves in the dual space. VLDB J. 17, 379–400 (2008)CrossRefGoogle Scholar
  44. 44.
    Zhang R., Jagadish H.V., Dai B.T., Ramamohanarao K.: Optimized algorithms for predictive range and knn queries on moving objects. Inf. Syst. 35(8), 911–932 (2010)CrossRefGoogle Scholar
  45. 45.
    Zhang, R., Lin, D., Kotagiri, R., Bertino, E.: Continuous intersection joins over moving objects. In: Proceedings of ICDE, pp. 863–872 (2008)Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Rui Zhang
    • 1
    Email author
  • Jianzhong Qi
    • 1
  • Dan Lin
    • 2
  • Wei Wang
    • 3
  • Raymond Chi-Wing Wong
    • 4
  1. 1.University of MelbourneParkvilleAustralia
  2. 2.Missouri University of Science and TechnologyRollaUSA
  3. 3.University of New South WalesKensingtonAustralia
  4. 4.Hong Kong University of Science and TechnologyClear Water BayHong Kong

Personalised recommendations