Spatial Information Research

, Volume 27, Issue 5, pp 565–571 | Cite as

Improving the performance of location based spatial textual query processing using distributed strip index

  • M. PriyaEmail author
  • R. Kalpana


Location Based Services are information retrieval services that offer accurate information required by the end user. These services are the query based services accessed mainly through mobile devices and have number of uses in social networking for providing entertainment, business and healthcare information. In health care system, if a person wants to get immediate medical help at any place, he needs to access a medical database with the help of location-based query. Sometimes, location-based query can associate with the text information, such as the user wants to find the nearest hospital with the facility of pharmacy or ambulance. This type of query has to resolve both location and textual information. This paper proposes a new distributed index structure to resolve location-based query, and introduces a new probabilistic mechanism to correct the typographical errors when retrieving the documents. The experimental results show that the distributed strip index structure produces better performance than the existing distributed R tree structure.


Data space Location-based query Medical databases Box space Divide and conquer Maximum-a-likelihood 



  1. 1.
    Osborn, W., & Hinze, A. (2007). Issues in location based indexing for co-operating mobile information systems. In R. Meersman, Z. Tari, & P. Herrero (Eds.), On the move to meaningful internet system 2007, OTM 2007 workshops, OTM 2007 lecture notes in computer science (vol. 4805). Berlin: Springer.Google Scholar
  2. 2.
    Mouratidis, K., Bakiras, S., & Papadias, D. (2009). Continuous monitoring of spatial queries in wireless broadcast environments. IEEE Transaction on Mobile Computing, 8(10), 1297–1311.CrossRefGoogle Scholar
  3. 3.
    Naresh, K., Thangakumar, J., & Pannem, D. (2012). Spatial query monitoring in wireless broadcast environment. In IEEE international conference on internet computing and information communication (ICICI) (pp. 35–42). Berlin: Springer. ISBN: 978-81-322-1299-7.Google Scholar
  4. 4.
    Lin, L., Cai, Y. Z., & Xu, Z. (2008). Spatial temporal indexing mechanisms based on snapshot increment. Advanced in spatial temporal analysis. London: Taylor and Francis group.Google Scholar
  5. 5.
    Zhong, Y., Han, J., Zhang, T., Li, Z., Fang, J., & Chen, G. (2012, May). Towards parallel spatial query processing for big spatial data. In IEEE 26th international conference on parallel and distributed processing symposium workshops & PhD forum (IPDPSW) (pp. 2085–2094).Google Scholar
  6. 6.
    Zhang, C., Li, F., & Jestes, J. (2012, March). Efficient parallel kNN joins for large data in MapReduce. In Proceedings of the 15th international conference on extending database technology (pp. 38–49). New York City: ACM. ISBN: 978-1-4503-0790-1.Google Scholar
  7. 7.
    Yu, Z., & Liu, Y. (2015). Scalable distributed processing of K nearest neighbor queries over moving objects. IEEE Transaction on Knowledge and Data Engineering, 7(5), 1383–1396.CrossRefGoogle Scholar
  8. 8.
    Zheng, B., Xu, J., Lee, W. C., & Lee, L. (2006). Grid-partition index: A hybrid method for nearest-neighbour queries in wireless location based services. VLDB Journal, 15, 21–39. Scholar
  9. 9.
    Eldawy, A., & Mokbel, M. F. (2015, April). SpatialHadoop: A MapReduce framework for spatial data. In IEEE international conference on data engineering (pp. 1352–1363).Google Scholar
  10. 10.
    Akdogan, A., Demiryurek, U., Banaei-Kashani, F., and Shahabi, C. (2010, November). Voronoi-based geospatial query processing with MapReduce. In IEEE second conference on cloud computing technology and science (pp. 9–16).Google Scholar
  11. 11.
    Zhang, J., Chen, X. L., Zhong, C., Wu, H., and Duan, S. (2008, July). Application of geo-spatial information technology in the engineering manage of roller compaction construction. In IEEE international conference on geoscience and remote sensing symposium (vol. 3, pp. 1312–1315).Google Scholar
  12. 12.
    Xuan, K., Zhao, G., Taniar, D., Srinivasan, B., Safar, M., and Gavrilova, M. (2009). Network Voronoi diagram based range search. In IEEE 23rd international conference on advanced information networking and applications (pp. 741–748).Google Scholar
  13. 13.
    Hariharan, R., Hore, B., Li, C., & Mehrotra, S. (2007, July). Processing spatial keyword queries in geographic information retrieval systems. In IEEE international conference on scientific and statistical database management (p. 16). ISSN: 1551-6393.Google Scholar
  14. 14.
    Akiba, T., Iwata, Y., & Yoshida, Y. (2014, April). Dynamic and historical shortest-path distance queries on large evolving networks by pruned landmark labeling. In Proceedings of 23rd international conference on world wide web (pp. 237–248). New York City: ACM.Google Scholar
  15. 15.
    Suwardi, I. S., Dharma, D., Satya, D. P., & Lestari, D. P. (2015, August). Geohash index based spatial data model for corporate. In International conference on electrical engineering and informatics (pp. 478–483). ISSN: 2155-6830.Google Scholar

Copyright information

© Korean Spatial Information Society 2019

Authors and Affiliations

  1. 1.Bharathiyar College of Engineering and TechnologyKaraikalIndia
  2. 2.Pondicherry Engineering CollegePuducherryIndia

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