SR-Tree: An Index Structure of Sensor Management System for Spatial Approximate Query

Abstract:

Sensor management plays an important role in the field of Internet of Things. Therefore, the requests of spatial approximate query increase dramatically. Indexing is no doubt a feasible way for efficient spatial approximate search. However, there is a lack of an effective index structure for spatial approximate query. In this paper, we propose a new type of index structure called SR-tree for providing more intelligent retrieval, which is based on R-tree and inverted table. Our index can support for spatial approximate search and work freely either in memory or external memory. The experimental results show that the structure proposed can provide high scalability and fast response time.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 885-889
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.885
• Online since: September 2013
• Authors: Xing Tong, Yang Liu, Zhao Shi, Peng Zeng, Hai Bin Yu
• Keywords: Index Structure, Internet of Things (IOT), Sensor Management System

References

[1] Li JianZhong, Li JinBao, and Shi ShengFei, Concepts, Issues and Advance of Sensor Networks and Data Management of Sensor Networks, Journal of Software, vol. 14, 2003, pp.1717-1727.

[2] A. Guttman, R-trees: a dynamic index structure for spatial searching, Proc. Special Interest Group On Management Of Data, (1984).

[3] N. Beckmann, H. P. Kriegel, R. Schneider, and B. Seeger, The R∗-tree: an efficient and robust access method for points and rectangles, Proc. Special Interest Group On Management Of Data, (1990).

DOI: 10.1145/93597.98741

[4] Zobel, Justin, Moffat, etc, Inverted files versus signature files for text indexing, ACM Transactions on Database Systems (TODS), vol. 23, 1998, pp.453-490.

DOI: 10.1145/296854.277632

[5] Sattam Alsubaiee, Alexander Behm, and Chen Li, Supporting Location-Based Approximate-Keyword Queries, Proc. The 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM Press, 2010, pp.61-70.

DOI: 10.1145/1869790.1869802

[6] Bin Yao, Feifei Li, Marios Hadjieleftheriou, and Kun Hou, Approximate string search in spatial databases, Proc. The 26th International Conference on Data Engineering, IEEE Press, 2010, pp.545-556.

DOI: 10.1109/icde.2010.5447836

[7] Ian De Felipe, Vagelis Hristidis, and Naphtali Rishe, Keyword Search on Spatial Databases, Proc. The 24th International Conference on Data Engineering, IEEE Press, 2008, pp.656-665.

DOI: 10.1109/icde.2008.4497474

[8] Chen Li, Jiaheng Lu, and Yiming Lu, Efficient merging and filtering algorithms for approximate string searches, Proc. The 24th International Conference on Data Engineering, IEEE Press, 2008, pp.257-266.

DOI: 10.1109/icde.2008.4497434