Abstract

Although lot of spatio-temporal indexing techniques for moving objects are availed, some more intelligence has been given to the advance of techniques that competently support queries about the past, present, and future positions of moving objects. This paper proposes the new index structure called SOBBx (Space Based Optimal BBx) which indexes the positions of moving objects, given as linear functions of time, at any time. In a Time t, more objects are updated to the tree than usual. It saves the cost of regular update as well. The simulation results shows that the proposed algorithm provides superior performance than POBBx index structure.

References

• Long-Van Nguyen-Dinh, Walid G. Aref, Mohamed F. Mokbel 2010. Spatio-Temporal Access Methods: Part 2 (2003 - 2010). Bulletin of the IEEE Computer SocietyTechnical Committee on Data Engineering
• M. Pelanis, S. ? Saltenis, and C. Jensen. Indexing the past, present, and anticipated future positions of moving objects. TODS, 31(1):255–298, 2006.
• Z. -H. Liu, X. -L. Liu, J. -W. Ge, and H. -Y. Bae. Indexing large moving objects from past to future with PCFI+-index. In COMAD, pages 131–137, 2005.
• V. Chakka, A. Everspaugh, and J. Patel. Indexing large trajectory data sets with SETI. In CIDR, 2003
• Y. Tao, D. Papadias, and J. Sun. The TPR*-tree: An optimized spatio-temporal access method for predictive queries. In VLDB, 2003.
• C. Jensen, D. Lin, and B. Ooi. Query and update efficient B+-tree based indexing of moving objects. In VLDB, 2004
• M. Mokbel, T. Ghanem, andW. G. Aref. Spatio-temporal access methods. IEEE Data Eng. Bull. , 26(2):40–49, 2003.
• J. Ni and C. V. Ravishankar. PA-tree: A parametric indexing scheme for spatio-temporal trajectories. In SSTD, 2005.
• P. Zhou, D. Zhang, B. Salzberg, G. Cooperman, and G. Kollios. Close pair queries in moving object databases. In GIS, pages 2–11, 2005.
• Dan Lin, Christian S. Jensen, Beng Chin Ooi, Simonas S? altenis, BBx index :Efficient Indexing of the Historical, Present, and Future Positions of Moving Objects, MDM 2005 Ayia Napa Cyprus
• P. K. Agarwal and C. M. Procopiuc. Advances in Indexing for Mobile Objects. IEEE Data Eng. Bull. , 25(2): 25–34, 2002.
• G. Kollios, D. Gunopulos, V. J. Tsotras. On Indexing Mobile Objects. In Proc. PODS, pp. 261–272, 1999.
• K. Appathurai, Dr. S. Karthikeyan. A Survey on Spatiotemporal Access Methods. International Journal of Computer Appliations. Volume 18, No 4, 2011.
• Mohamed F. Mokbel, Xiaopeng Xiong, oustafa A. Hammad, and Walid G. Aref, Continuous Query Processing of Spatio-temporal Data Streams in PLACE, 2004 Kluwer Academic Publishers. Printed in the Netherlands
• Su Chen • Beng Chin Ooi • Zhenjie Zhang, An Adaptive Updating Protocol for Reducing Moving Object Database Workload.
• Yongquan Xia, Weili Li , and Shaohui Ning, Moving Object Detection Algorithm Based on Variance Analysis, 2009, Second International Workshop on Computer Science and Engineering Qingdao, China
• Arash Gholami Rad, Abbas Dehghani and Mohamed Rehan Karim, Vehicle speed detection in video image sequences using CVS method, 2010, International Journal of the Physical Sciences Vol. 5(17), pp. 2555-2563.
• M. A. Nascimento and J. R. O. Silva. owards Historical R-trees. In Proc. ACM Symposium on Applied Computing, pp. 235–240, 1998.
• Y. Tao and D. Papadias. MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries. In Proc. VLDB, pp. 431–440, 2001.
• J. Sun, D. Papadias, Y. Tao, and B. Liu. Querying about the Past, the Present, and the Future in Spatio-Temporal Databases. In Proc. ICDE, pp. 202–213, 2004.
• K. Appathurai, Dr. S. Karthikeyan 2012, "A New Proposed Algorithm for OBBx-index Structure", International Journal of Computer Applications, Vol. 50
• , 0975 – 8887.
• SU CHEN, BENG CHIN OOI and KIAN-LEE TAN, "Continuous Online Index Tuning in Moving Object Databases", ACM Transactions on Database Systems, Vol. V, No. N, Month 20YY, Pages 1–45.