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Indexing hs code- a hybrid indexer for an optimized search of geotagged data

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In case of unstructured data processing current technologies have developed a lot of solutions to process and provide insight into it, which has become paramount today due to the permeation of social media. Social media analytics extract data points by searching the most relevant textual reference and isolating textual data with location information. This increase the complexity of modern social media search engines. The indexer is imperative towards the performance of these engines. Geotagged data generated via modern social media technologies has augmented the need to enhance such search mechanisms designed for spatial data.Conventional spatial indexers designed to handle such data can accurately search spatial objects but with a considerable increase in seek time. This paper presents a hybrid spatial indexer based on “Hs-I” tree for the “Social Media Spatial Analytical (SMSA)” model. The purposed indexer is 17.768% faster when compared with the “Geo-hash” indexer. The model refers “CBDFI” model for base architecture and deploys the advantage of the “hs” code. The paper presents the comparison of the purposed indexer with various other spatial indexers and highlights its key points in terms of execution time.

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References

  1. Lee, I. (2018). Social media analytics for enterprises: Typology, methods, and processes. Business Horizons., 61(2), 199–210. https://doi.org/10.1016/j.burshor.2017.11.00

    Article  Google Scholar 

  2. Mansfield, M. (2016). “Social media statistics 2016. Small Business Trends.” https://smallbiztrends.com/2016/11/social-media-statistics-2016.html.

  3. Schade, S. (2015). Big data breaking barriers--first steps on a long trail. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. https://doi.org/10.5194/isprsarchives-XL-7-W3-691-2015

  4. Zhang, N., Zheng, G., Chen, H., Chen, J., & Chen, X. (2014 , september). Hbasespatial: A scalable spatial data storage based on hbase. In 2014 IEEE 13th international conference on trust, security and privacy in computing and communications (pp. 644–651). IEEE. https://doi.org/10.1109/trustcom.2014.83

  5. Singh B, Srivastava K, Gupta DK. Unique indexing model in Geospatial database Paradigm. Indian Journal of Computer Science and Engineering (IJCSE) e-ISSN : 0976–5166, p-ISSN: 2231–3850. Vol. 11 No 2 (2020): 204–216.

  6. Rathore, A. K., Kar, A. K., & Ilavarasan, P. V. (2017). Social media analytics: Literature review and directions for future research. Decision Analysis., 14(4), 229–249. https://doi.org/10.1287/deca.2017.035514(4)

    Article  Google Scholar 

  7. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons., 53(1), 59–68. https://doi.org/10.1016/j.bushor.2009.09.00353(1)

    Article  Google Scholar 

  8. Faulds, M. W. G., & DJ,. (2009). Social media: The new hybrid element of the promotion mix. Kelley School of Business. Indiana University. Business Horizons., 52, 357–365. https://doi.org/10.1016/j.bushor.2009.03.00252(4)

    Article  Google Scholar 

  9. Grubmüller, V., Götsch, K., & Krieger, B. (2013). Social media analytics for future oriented policy making. European Journal of Futures Research., 1(1), 1–9. https://doi.org/10.1007/s40309-013-0020-7

    Article  Google Scholar 

  10. Rigaux, P., Scholl, M., & Voisard, A. (2002). Spatial databases: with application to GIS. Morgan Kaufmann. https://doi.org/10.1016/B978-155860588-6/50008-7.

  11. Chen, Y.Y., Suel, T., & Markowetz A. (2006, june) .Efficient query processing in geographic web search engines. In Proceedings of the 2006 ACM SIGMOD international conference on Management of data  (pp. 277–288). https://doi.org/10.1145/1142473.1142505.

  12. Li, Z., Lee, K. C., Zheng, B., Lee, W. C., Lee, D., & Wang, X. (2010). Ir-tree: An efficient index for geographic document search. IEEE transactions on knowledge and data engineering. 23(4), 585–599. https://doi.org/10.1109/tkde.2010.14923(4)

    Article  Google Scholar 

  13. De Felipe I., Hristidis, V., & Rishe, N. (2008 , April). Keyword search on spatial databases. In 2008 IEEE 24th International Conference on Data Engineering (pp. 656–665). IEEE. https://doi.org/10.1109/icde.2008.4497474.

  14. Christoforaki, M., He, J., Dimopoulos, C., Markowetz, A., & Suel, T. (2011, October). Text vs. space: efficient geo-search query processing. In Proceedings of the 20th ACM international conference on Information and knowledge management  (pp. 423–432).

  15. Zhang, Y., Gao, Q., Gao, L., Wang, C., (2011 , October). PrIter: A distributed framework for prioritized iterative computations. In Proceedings of the 2nd ACM Symposium on Cloud Computing  (pp. 1–14).

  16. Wu, D., Yiu.,M.L., Jensen, C.S., & Cong, G,.(2011 , April ). Efficient continuously moving top-k spatial keyword query processing. IN 2011 IEEE 27th International Conference on Data Engineering (pp. 541–552). IEEE.

  17. Zhou, Y., Xie, X., Wang, C., Gong, Y., M.a., W.Y., (2005 , October). Hybrid index structures for location-based web search. In Proceedings of the 14th  ACM international conference on Information and knowledge management  (pp. 155–162). https://doi.org/10.1145/1099554.1099584.

  18. Cong, G., Jensen, C. S., & Wu, D. (2009). Efficient retrieval of the top-k most relevant spatial web objects. Proceedings of the VLDB Endowment., 2(1), 337–348. https://doi.org/10.14778/1687627.1687666

    Article  Google Scholar 

  19. Cary, A., Wolfson, O., Rishe, N., (2010 , June). Efficient and scalable method for processing top-k spatial boolean queries. In International Conference on Scientific and Statistical Database Management  https://doi.org/10.1007/978-3-642-13818-8_8. (pp. 87–95). Springer, Berlin, Heidelberg.

  20. Hjaltason, G. R., & Samet, H. (1999). Distance browsing in spatial databases. ACM Transactions on Database Systems (TODS)., 24(2), 265–318. https://doi.org/10.1145/320248.32025524(2)

    Article  Google Scholar 

  21. Rocha-Junior, J.B., Gkorgkas, O., Jonassen, S., Nørvåg, K., (2011 , Aug 24). Efficient processing of top-k spatial keyword queries. InInternational Symposium on Spatial and Temporal Databases. https://doi.org/10.1007/978-3-642-22922-0_13.(pp. 205–222). Springer, Berlin, Heidelberg.

  22. Zheng, K., Gu, D., Fang, F., Zhang, M., Zheng, K., & Li, Q. (2017). Data storage optimization strategy in distributed column-oriented database by considering spatial adjacency. Cluster Computing, 20(4), 2833–2844. https://doi.org/10.1007/s10586-017-1081-3

    Article  Google Scholar 

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Acknowledgements

This work is presented in continuation with my PhD research work at the University of petroleum and Energy studies. I thank my Guide Dr. Kingshuk Srivatava and Co- guide Dr, Dhermendra Kumar Gupta for their guidance and motivation to conduct this research. I thank Dr. Tanupriya Choudary for his guidance in writing this paper. We thank School of computer science and School of Engineering for providing us with relevant resource to conduct this study

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Authors and Affiliations

  1. Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, India

    Bhagwant Singh & Kingshuk Srivastava

  2. Department of Petroleum Engineering and Earth Sciences, School of Engineering, University of Petroleum and Energy Studies (UPES), Dehradun, India

    D. K. Gupta

  3. School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, India

    Tanupriya Choudhury

  4. Department of Geography, College of Social Sciences, Kyungpook National University, 80, University Road, Buk-gu, Daegu, 702-701, South Korea

    Jung-Sup Um

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  1. Bhagwant Singh
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  2. Kingshuk Srivastava
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  3. D. K. Gupta
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  4. Tanupriya Choudhury
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  5. Jung-Sup Um
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Correspondence to Tanupriya Choudhury.

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I declare that this paper is an original work of our research and has not been submitted for publication in any other journal. The experimental work has entirely been done by us and if any collaborative work is included it is clearly identified and acknowledge. For this research work no external funding from any external agency has been received by us.

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Singh, B., Srivastava, K., Gupta, D.K. et al. Indexing hs code- a hybrid indexer for an optimized search of geotagged data. Spat. Inf. Res. 31, 1–13 (2023). https://doi.org/10.1007/s41324-022-00473-2

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  • DOI: https://doi.org/10.1007/s41324-022-00473-2

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