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Multi-keyword score threshold and B+ tree indexing based top-K query retrieval in cloud

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Abstract

Cloud computing is an emerging technology where computing resources are delivered as a service over a network which is accessed by many cloud users. Cloud services on the real-world application attain the fundamental resource sharing and low-cost preserving characteristics. While increasing the number of user requests, the most significant deal is the identification and retrieval of top-k queries in cloud environments. Several techniques have been developed to retrieve the top-k queries, but effective modeling of query result retrieval on cloud services with less complexity is not attained. In order to improve the query result retrieval rate, Top-k Query Multi-Keyword Score Threshold (Top-k QMKST) technique is developed. This technique considers four processes for retrieving the top-k results in minimum time. At first, multiple keywords are extracted from the query, and then the B+ tree indexing is used to index the data with the objective of reducing the response time and space complexity. Third, a score value is calculated using Kullback–Leibler Divergence which provides the probable results of keywords occurrences among a collection of keywords in an index list. At last Monotonic weighted score aggregation function is used for assigning the weight to the resultant content score. Experimental evaluation is carried out with different parameters and the results showed that the Top-k QMKST technique is better in case of query result retrieval with minimum false positive rate, reduced response time and space complexity.

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References

  1. 1.

    Santoso BJ, Chiu G-M (2014) Close dominance graph: an efficient framework for answering continuous top-k dominating queries. IEEE Trans Knowl Data Eng 26(8):1853–1865

  2. 2.

    Li R, Xu Z, Kang W, Yow KC, Xu C-Z (2014) Efficient multi-keyword ranked query over encrypted data in cloud computing. Futur Gener Comput Syst 30:179–190

  3. 3.

    Anteneh Ayanso, Paulo B. Goes, Kumar Mehta, “Range query estimation with data skewness for top-k retrieval”, Decis Support Syst, Volume 57, January 2014, Pages 258–273

  4. 4.

    PengPeng, Lei Zoubc, Zheng Qina, “Answering top-K query combined keywords and structural queries on RDF graphs”, Inf Syst, Volume 67, July 2017, Pages 19–35

  5. 5.

    Jongwuk Lee, Hyunsouk Cho, Sunyou Lee, and Seung-won Hwang, “Toward scalable indexing for top-k queries”, IEEE Trans Knowl Data Eng, Volume 26, Issue 12, 2014, Pages 3103–3116

  6. 6.

    Yu J, Lu P, Zhu Y, Xue G, Li M (2013) Toward secure multikeyword top-k retrieval over encrypted cloud data. IEEE Trans Dependable Secure Comput 10(4):239–250

  7. 7.

    Sun-Young Ihm, Aziz Nasridinov, and Young-Ho Park, “Grid-PPPS: a skyline method for efficiently handling top-k queries in internet of things”, Hindawi Publishing Corporation Journal of Applied Mathematics, Volume 2014, May 2014, Pages 1–10

  8. 8.

    Kamel I, Talha AM, Al Aghbari Z (2017) Dynamic spatial index for efficient query processing on the cloud. Journal of Cloud Computing Advances, Systems and Applications, Springer 6(5):1–16

  9. 9.

    Jongwuk Lee, Dongwon Lee, Seung-won Hwang, “CrowdK: answering top-k queries with crowd sourcing”, Inf Sci, Elsevier, Volume 399, August 2017, Pages 98–120

  10. 10.

    Zhangjie Fu, Xingming Sun, Nigel Linge, Lu Zhou, “Achieving effective cloud search services: multi-keyword ranked search over encrypted cloud data supporting synonym query”, IEEE Trans Consum Electron, Volume. 60, Issue. 1, February 2014, Pages 164–172

  11. 11.

    Lu H, Foh CH, Wen Y, Cai J (2017) Delay-optimized file retrieval under LT-based cloud storage. IEEE Transactions on Cloud Computing 5(4):656–666

  12. 12.

    Li G, Feng J, Zhou X, Wang J (2011) Providing built-in keyword search capabilities in RDBMS. The VLDB Journal, Springer 20(1):1–19

  13. 13.

    Tian Guo, Thanasis G. Papaioannou, Karl Aberer, “Efficient indexing and query processing of model-view sensor data in the cloud”, Big Data Research, Elsevier, Volume 1, August 2014, Pages 52–65

  14. 14.

    Xiaofeng Ding, Peng Liu, Hai Jin, “Privacy-preserving multi-keyword top-k similarity search over encrypted data”, IEEE Trans Dependable Secure Comput, Volume PP, Issue 99, Pages 1–14

  15. 15.

    Jiang X, Yu J, Yan J, Hao R (2017) Enabling efficient and verifiable multi-keyword ranked search over encrypted cloud data. Inf Sci, Elsevier 403–404:22–41

  16. 16.

    Rodríguez-García MÁ, Valencia-García R, García-Sánchez F, Javier Samper-Zapater J (2014) Ontology-based annotation and retrieval of services in the cloud. Knowl-Based Syst, Elsevier 56:15–25

  17. 17.

    Liu X, Wan C, Chen L (2011) Returning clustered results for keyword search on XML documents. IEEE Trans Knowl Data Eng 23(12):1811–1825

  18. 18.

    Xu J, Lu H (2017) Efficiently answer top-k queries on typed intervals. Comput Secur, Elsevier 69:84–96

  19. 19.

    Hailong Sun, Yu Tang, Qi Wang, Xudong Liu, “Handling multi-dimensional complex queries in key-value data stores”, Inf Syst, Elsevier, Volume 66, June 2017, Pages 82–96

  20. 20.

    Mohamed Shakeel, P., Baskar, S. & Selvakumar, S. Wireless Pers Commun (2019). Retrieving multiple patient information by using the virtual MIMO and path Beacon in wireless body area network, pp 1-12. https://doi.org/10.1007/s11277-019-06525-5

  21. 21.

    Negi, D., Ray, S., & Lu, R. (2019). Pystin: enabling secure LBS in smart cities with privacy-preserving top-k spatial-textual query. IEEE Internet of Things Journal

  22. 22.

    Yang J, Zhang Y, Zhou X, Wang J, Hu H, Xing C (2019, April) A hierarchical framework for top-k location-aware error-tolerant keyword search. In: 2019 IEEE 35th international conference on data engineering (ICDE). IEEE, pp 986–997

  23. 23.

    MuhammedShafi. P,Selvakumar.S*, Mohamed Shakeel. P, “An Efficient Optimal Fuzzy C Means (OFCM) Algorithm with particle swarm optimization (PSO) to analyze and predict crime data”, Journal of Advanced Research in Dynamic and Control Systems, Issue: 06,2018, Pages: 699–707

  24. 24.

    Chen C, Zhu X, Shen P, Hu J, Guo S, Tari Z, Zomaya AY (2016) An efficient privacy-preserving ranked keyword search method. IEEE Trans Parallel Distrib Syst 27(4):951–963

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Correspondence to K. Karthika Lekshmi.

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Lekshmi, K.K., Prem, M.V. Multi-keyword score threshold and B+ tree indexing based top-K query retrieval in cloud. Peer-to-Peer Netw. Appl. (2019). https://doi.org/10.1007/s12083-019-00794-4

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Keywords

  • Cloud computing
  • Top-k query retrieval
  • B+ tree index
  • Kullback–Leibler divergence
  • Score threshold
  • Monotonic weighted score aggregation function