Querying Unintelligible Data on Geospatial Trajectory Database

Authors(2) :-Dr. K. Sathesh Kumar, Dr. S. Ramkumar

Current GPS technologies collect objects and its movement and store the trajectories periodically in the MOD (Moving Object Database). In such environment, some location errors may arise and some models are unable to capture the changes in trajectories dynamically. Especially, the uncertainty capturing is a challenging one. In order to handle these issues in spatial database, the proposed system develops a new trajectory model to handle the uncertainty and querying on uncertain spatial queries. Initially this develops an adaptable trajectory approach to provide actual positions and temporal changes in uncertainty along with improbable uncertainty ranges. The next part of ongoing implementation provides effective spatial query processing with successful indexing process. This presents the temporal R+ tree indexing with inverted list. This provides an efficient mechanism to evaluate improbable range objects and its spatial queries using Rife-density trajectories.

Authors and Affiliations

Dr. K. Sathesh Kumar
Assistant Professor, Department of Computer Science and Information Technology, Kalasalingam University, Krishnankoil, Virudhunagar, Tamil Nadu, India
Dr. S. Ramkumar
Assistant Professor, Department of Computer Applications, Kalasalingam University, Krishnankoil, Virudhunagar, Tamil Nadu, India

RFID sensors, R+Tree, GSM, Trajectory Model, Road Network, Dynamic Route Map.

  1. Brakatsoulas, Sotiris, et al. "On map-matching vehicle tracking data." Proceedings of the 31st international conference on Very large data bases. VLDB Endowment, 2005.
  2. Li, Xu, et al. "A practical map-matching algorithm for GPS-based vehicular networks in Shanghai urban area." Wireless, Mobile and Sensor Networks, 2007.(CCWMSN07). IET Conference on. IET, 2007.
  3. Kuijpers, Bart, and Walied Othman. "Trajectory databases: Data models, uncertainty and complete query languages." Database Theory–ICDT 2007. Springer Berlin Heidelberg, 2006. 224-238.
  4. Liu, Hechen, and Markus Schneider. "Querying moving objects with uncertainty in spatio-temporal databases." Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2011.
  5. Hajari, Hadi, and Farshad Hakimpour. "A Spatial Data Model for Moving Object Databases." arXiv preprint arXiv:1403.3304 (2014).
  6. Karthikeyani, V., and I. ShahinaBegam. "Different Direction Flow Analysation Algorithm (DDFAA) Of Moving Object Using Spatial
  7. Trajcevski, Goce, et al. "The geometry of uncertainty in moving objects databases." Advances in Database Technology—EDBT 2002. Springer Berlin Heidelberg, 2002. 233-250.
  8. Pelekis, Nikos, et al. "Clustering uncertain trajectories." Knowledge and Information Systems1 (2011): 117-147.
  9. Diebold, T. Gunther, and A. Tay, “Evaluating density forecasts with applications to financial risk management,” Int. Econ. Rev., vol. 39, no. 4, pp. 863–883, Nov. 1998.
  10. Ding, “UTR-Tree: An index structure for the full uncertain trajectories of network-constrained moving objects,” in Proc. 9th Int. Conf. MDM, Beijing, China, 2008, pp. 33–40.
  11. Ding and R. H. Güting, “Uncertainty management for network constrained moving objects,” in Proc. 15th Int. Conf. DEXA, Berlin, Heidelberg, Germany, 2004, pp. 411–421.
  12. Frentzos, K. Gratsias, and Y. Theodoridis, “On the effect of location uncertainty in spatial querying,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 3, pp. 366–383, Mar. 2009.
  13. Hightower and G. Borriello, “Location systems for ubiquitous computing,” IEEE Comput., vol. 34, no. 8, pp. 57–66, Aug. 2001.
  14. Hornsby and M. J. Egenhofer, “Modeling moving objects over multiple granularities,” Ann. Math. Artif. Intell., vol. 36, no. 1–2, pp. 177–194, Sept. 2002.
  15. Jain, E. Y. Chang, and Y.-F. Wang, “Adaptive stream resource management using Kalman filters,” in 2004 ACM SIGMOD Int. Conf. Manage. Data, pp. 11–22.
  16. A S. Jensen, H. Lahrmann, S. Pakalnis, and J. Runge, “The INFATI data.” TIMECENTER, Tech. Rep. TR-79. 2008.
  17. Jeung, H. T. Shen, and X. Zhou, “Mining trajectory patterns using hidden Markov models,” in Proc. 9th Int. Conf. DaWaK, Regensburg, Germany, 2007, pp. 470–480.
  18. Kanagal and A. Deshpande, “Online filtering, smoothing and probabilistic modeling of streaming data,” in Proc. 2008 IEEE 24th ICDE, Cancun, Mexico, pp. 1160–1169.
  19. E. Knuth, The Art of Computer Programming, Vol. 3, Sorting and Searching, 2nd ed. Redwood City, CA, USA: Addison Wesley Longman Publishing Co., Inc., 1998.
  20. Kuijpers, B. Moelans, W. Othman, and A. A. Vaisman, “Analyzing trajectories using uncertainty and background information,” in Proc. 11th Int. Symp. SSTD, Aalborg, Denmark, 2009, pp. 135–152.
  21. Kuijpers and W. Othman, “Trajectory databases: Data models, uncertainty and complete query languages,” J. Comput. Syst. Sci., vol. 76, no. 7, pp. 538–560, Nov. 2010.
  22. LaMarca and E. de Lara, Location Systems: An Introduction to the Technology behind Location Awareness. San Rafael, CA, USA: Morgan and Claypool Publishers, 2008.
  23. Liu and M. Schneider, “Querying moving objects with uncertainty in spatio-temporal databases,” in Proc. 16th Int. Conf. DASFAA, Hong Kong, China, 2011, pp. 357–371.
  24. Lu, B. Yang, and C. S. Jensen, “Spatio-temporal joins on symbolic indoor tracking data,” in Proc. IEEE 27th ICDE, Hannover, Germany, 2011, pp. 816–827.
  25. J. Miller, “A measurement theory for time geography,” Geographical Anal., vol. 37, no. 1, pp. 17–45, 2005
  26. Emrich, T., Kriegel, H. P., Mamoulis, N., Niedermayer, J., Renz, M., & Züfle, A. (2014, April). Reverse-nearest neighbor queries on uncertain moving object trajectories. In International Conference on Database Systems for Advanced Applications(pp. 92-107). Springer International Publishing.
  27. Eleazar Leal, Le Gruenwald, Jianting Zhang and Simin You, "TKSimGPU: A Parallel Top-K Trajectory Similarity Query Processing Algorithm for GPGPUs", Proceedings of the 2015 IEEE International Big Data Conference, Oct 29-Nov 1 2015, Santa Clara
  28. Jianting Zhang, Simin You and Le Gruenwald, "Spatial Join Query Processing in Cloud: Analyzing Design Choices and Performance Comparisons", Proceedings of High Performance Computing for Big Data Workshop (HPC4BD'15), colocated with the 44rd International Conference on Parallel Processing (ICPP), Sept 1-4, 2015

Publication Details

Published in : Volume 2 | Issue 4 | July-August 2016
Date of Publication : 2016-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 180-187
Manuscript Number : IJSRST162437
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Dr. K. Sathesh Kumar, Dr. S. Ramkumar, " Querying Unintelligible Data on Geospatial Trajectory Database", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 2, Issue 4, pp.180-187, July-August-2016.
Journal URL : http://ijsrst.com/IJSRST162437

Article Preview