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Efficient Maintenance of Continuous Queries for Trajectories

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Abstract

We address the problem of optimizing the maintenance of continuous queries in Moving Objects Databases, when a set of pending continuous queries need to be reevaluated as a result of bulk updates to the trajectories of moving objects. Such bulk updates may happen when traffic abnormalities, e.g., accidents or road works, affect a subset of trajectories in the corresponding regions, throughout the duration of these abnormalities. The updates to the trajectories may in turn affect the correctness of the answer sets for the pending continuous queries in much larger geographic areas. We present a comprehensive set of techniques, both static and dynamic, for improving the performance of reevaluating the continuous queries in response to the bulk updates. The static techniques correspond to specifying the values for the various semantic dimensions of trigger execution. The dynamic techniques include an in-memory shared reevaluation algorithm, extending query indexing to queries described by trajectories and query reevaluation ordering based on space-filling curves. We have completely implemented our system prototype on top of an existing Object-Relational Database Management System, Oracle 9i, and conducted extensive experimental evaluations using realistic data sets to demonstrate the validity of our approach.

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Notes

  1. Mapquest is an online map service provided by Time Warner through its AOL service, Yahoo! Maps is a similar service provided by Yahoo! Inc. and Google Maps by Google Inc.

  2. The specification of the SQL99 standard distinguishes between statement execution context, routine execution context, and trigger execution context [2].

  3. The detailed analysis of the impact of the coupling modes  [26] of the different parts of a particular trigger (Event, Condition or Action) among themselves and with the transaction that generated its enabling Event is beyond the scope of this article.

References

  1. DOMINO project at DBMC laboratory, UIC. http://www.cs.uic.edu/~wolfson/html/mobile.html .

  2. ANSI/ISO international standard: Database language SQL. http://webstore.ansi.org .

  3. Forbes online issue, April 13, 2006. http://www.forbes.com/home/digitalentertainment/2006/04/13/google-aol-yahoo -cx_rr_0417maps.html .

  4. Geographic Data Technology. http://www.geographic.com .

  5. Oracle 9i. http://www.oracle.com/technology/products/oracle9i .

  6. H. Ding. Context Aware Optimization of Continuous Query Maintenance for Trajectories. M.S. thesis, Dept. of EECS, Northwestern University, 2005.

  7. N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. “ The R*-Tree: An efficient and robust access method for points and rectangles,” in Proc. of SIGMOD Conference, pp. 322–331, 1990.

  8. V.P. Chakka, A. Everspaugh, and J.M. Patel. “Indexing large trajectory data sets with SETI,” in Proc. of CIDR, 2003.

  9. E.W. Cheney and D.R. Kincaid. Numerical Mathematics and Computing. Brooks Cole, 2003.

  10. T.H. Cormen, C.E. Leiserson, and R.L. Rivest. Introduction to Algorithms. McGraw-Hill, NY, 1990.

    Google Scholar 

  11. H. Ding, G. Trajcevski, and P. Scheuermann. “Omcat: optimal maintenance of continuous queries’ answers for trajectories,” in Proc. of SIGMOD Conference, pp. 748–750, 2006.

  12. L. Forlizzi, R.H. Güting, E. Nardelli, and M. Schneider. “ A data model and data structures for moving objects databases,” in Proc. of SIGMOD Conference, pp. 319–330, 2000.

  13. P. Fraternali and L. Tanca. “ A structured approach for the definition of the semantics of active databases,” ACM Transactions on Database Systems, Vol. 20(4):414–471, 1995.

    Article  Google Scholar 

  14. B. Gedik and L. Liu. “Mobieyes: Distributed processing of continuously moving queries on moving objects in a mobile system,” in Proc. of EDBT, pp. 67–87, 2004.

  15. R.H. Güting, M.H. Böhlen, M. Erwig, C.S. Jensen, N.A. Lorentzos, M. Schneider, and M. Vazirgiannis. “A foundation for representing and querying moving objects,” ACM Transactions on Database Systems, Vol. 25(1):1.

  16. R.H. Güting and M. Schneider. Moving Object Databases. Morgan Kaufmann, CA, 2005.

    Google Scholar 

  17. A. Guttman. “R-trees: A dynamic index structure for spatial searching,” in B. Yormark (Ed.), SIGMOD’84, Proceedings of Annual Meeting, Boston, Massachusetts, June 18–21, 1984, pp. 47–57. ACM Press, 1984.

  18. M. Hadjieleftheriou, G. Kollios, V.J. Tsotras, and D. Gunopulos. “Efficient indexing of spatiotemporal objects,” in Proc. of EDBT, pp. 251–268, 2002.

  19. G.S. Iwerks, H. Samet, and K.Smith. “Continuous k-nearest neighbor queries for continuously moving points with updates,” in Proc. of VLDB, pp. 512–523, 2003.

  20. C.S. Jensen, D. Lin, and B.C. Ooi. “Query and update efficient b+-tree based indexing of moving objects,” in Proc. of VLDB, pp. 768–779, 2004.

  21. D.V. Kalashnikov, S. Prabhakar, S.E. Hambrusch, and W.G. Aref. “Efficient evaluation of continuous range queries on moving objects,” in DEXA, pp. 731–740, 2002.

  22. M. Koubarakis, T.K. Sellis, A.U. Frank, S. Grumbach, R.H. Güting, C.S. Jensen, N.A. Lorentzos, Y. Manolopoulos, E. Nardelli, B. Pernici, H.-J. Schek, M. Scholl, B. Theodoulidis, and N. Tryfona, (Eds.), Spatio-Temporal Databases: The CHOROCHRONOS Approach, volume 2520 of Lecture Notes in Computer Science. Springer, 2003.

  23. J.A.C. Lema, L. Forlizzi, R.H. Güting, E. Nardelli, and M. Schneider. “Algorithms for moving objects databases,” Computer Journalen, Vol. 46(6):680–712, 2003.

    Article  Google Scholar 

  24. M.F. Mokbel, X. Xiong, and W.G. Aref. SINA: Scalable incremental processing of continuous queries in spatio-temporal databases. in Proc. of SIGMOD Conference, pp. 623–634, 2004.

  25. B. Moon, H.V. Jagadish, C. Faloutsos, and J.H. Saltz. “Analysis of the clustering properties of the hilbert space-filling curve,” IEEE Transactions on Knowledge and Data Engineering, Vol. 13(1):124–141, 2001.

    Article  Google Scholar 

  26. N.W. Paton (Ed.), Active Rules in Database Systems. Springer, New York, 1999.

    Google Scholar 

  27. D. Pfoser and C.S. Jensen. “Trajectory indexing using movement constraints*,” GeoInformatica, Vol. 9(2):93–115, 2005.

    Article  Google Scholar 

  28. D. Pfoser, C.S. Jensen, and Y. Theodoridis. “Novel approaches in query processing for moving object trajectories,” in Proc. of VLDB, pp. 395–406, 2000.

  29. D. Pfoser, N. Tryfona, and C.S. Jensen. “Indeterminacy and spatiotemporal data: Basic definitions and case study,” GeoInformatica, Vol. 9(3):211–236, 2005.

    Article  Google Scholar 

  30. E. Pitoura and G. Samaras. Locating objects in mobile computing. IEEE Transactions on Knowledge and Data Engineering, Vol. 13(4):571–592, 2001.

    Article  Google Scholar 

  31. S. Prabhakar, Y. Xia, D.V. Kalashnikov, W.G. Aref, and S.E. Hambrusch. “Query indexing and velocity constrained indexing: Scalable techniques for continuous queries on moving objects,” IEEE Transactions on Computers, Vol. 51(10):1124–1140, 2002.

    Article  Google Scholar 

  32. S. Rasetic, J. Sander, J. Elding, and M.A. Nascimento. “A trajectory splitting model for efficient spatio-temporal indexing,” in Proc. of VLDB, pp. 934–945, 2005.

  33. R. Kothuri, A. Godfrind and E. Beinat. Pro Oracle Spatial. Apress, 2004.

  34. H. Sagan. Space-filling Curves. Springer, 1994.

  35. A.P. Sistla, O. Wolfson, S. Chamberlain, and S. Dao. “Modeling and querying moving objects,” in Proc. of ICDE, pp. 422–432, 1997.

  36. Y. Tao and D. Papadias. “Time-parameterized queries in spatio-temporal databases,” in Proc. of SIGMOD Conference, pp. 334–345, 2002.

  37. Y. Theodoridis, T.K. Sellis, A. Papadopoulos, and Y. Manolopoulos. “Specifications for efficient indexing in spatiotemporal databases,” in Proc. of SSDBM, pp. 123–132, 1998.

  38. G. Trajcevski, H.Ding, and P. Scheuermann. “Context-aware optimization of continuous range queries maintenance for trajectories,” in MobiDE, pp. 1–8, 2005.

  39. G. Trajcevski, O. Wolfson, K. Hinrichs, and S. Chamberlain. “Managing uncertainty in moving objects databases,” ACM Trans. Database Syst., Vol. 29(3):463–507, 2004.

    Article  Google Scholar 

  40. J. Widom and S. Ceri. Active Database Systems. 1995.

  41. O. Wolfson, A.P. Sistla, S. Chamberlain, and Y. Yesha. “Updating and querying databases that track mobile units,” Distributed and Parallel Databases, Vol. 7(3):257–387, 1999.

    Article  Google Scholar 

  42. X. Xiong, M.F. Mokbel, W.G. Aref, S.E. Hambrusch, and S. Prabhakar. “Scalable spatio-temporal continuous query processing for location-aware services,” in Proc. of SSDBM, p. 317, 2004.

  43. X. Xiong and W.G. Aref. “R-trees with update memos” in Proc. of ICDE, 2006.

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Authors and Affiliations

  1. Northwestern University, Evanston, IL, 60208, USA

    Hui Ding, Goce Trajcevski & Peter Scheuermann

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  1. Hui Ding
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  2. Goce Trajcevski
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  3. Peter Scheuermann
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Correspondence to Hui Ding.

Additional information

Research supported by the Northrop Grumman Corp., contract: P.O.8200082518.

Research supported partially by NSF grant IIS-0325144.

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Ding, H., Trajcevski, G. & Scheuermann, P. Efficient Maintenance of Continuous Queries for Trajectories. Geoinformatica 12, 255–288 (2008). https://doi.org/10.1007/s10707-007-0029-9

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