• E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

INTERNATIONAL JOURNAL OF INVENTIONS IN ENGINEERING & SCIENCE TECHNOLOGY

International Peer Reviewed (Refereed), Open Access Research Journal
(By Aryavart International University, India)

Paper Details

An In-Depth Study of The Keyword Search ‘Algorithmic Tools and Techniques’ in Cloud Data

Rishit Garkhel

Vandana International Sr. Sec School, Dwarka, New Delhi

1 - 6 Vol. 8, Jan-Dec, 2022
Receiving Date: 2022-12-11;    Acceptance Date: 2022-01-03;    Publication Date: 2022-01-10
Download PDF

Abstract

The owner of data likes to rethink archives in an encoded structure for protection safeguarding. Accordingly, it is fundamental to create productive and dependable ciphertext search methods. This paper proposes a progressive clustering strategy to help more semanticists meet the order for quick ciphertext search in a major data environment. The proposed progressive methodology clusters the reports based on the base importance edge. The outcomes show a sharp increment of reports in the informational collection. The query time of the proposed technique increments dramatically. Moreover, the proposed technique enjoys an upper hand over the traditional strategy in the work protection and importance of recovered statements.

    References

  1. DBLP computer science bibliography. http://dblp.uni-trier.de/.
  2. IMDB movie database. http://www.imdb.com
  3. Query templates. http://tinyurl.com/8zs3e77
  4. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the stateof-the-art and possible extensions. TKDE, 17(6):734–749, 2005.
  5. R. Agrawal, R. Rantzau, and E. Terzi. Context-sensitive ranking. In SIGMOD, pages 383–394, 2006.
  6. R. Agrawal and E. L. Wimmers. A framework for expressing and combining preferences. In SIGMOD, pages 297–306, 2000.
  7. A. Arvanitis and G. Koutrika. PrefDB: Bringing preferences closer to the DBMS. In SIGMOD, pages 665– 668, 2012.
  8. A. Arvanitis and G. Koutrika. Towards preference-aware relational databases. In ICDE, pages 426–437, 2012.
  9. S. Borzs ¨ onyi, D. Kossmann, and K. Stocker. The skyline operator. ¨ In ICDE, pages 421–430, 2001.
  10. J. Chomicki. Preference formulas in relational queries. TODS, 28(4):427–466, 2003.
  11. V. Christophides, D. Plexousakis, M. Scholl, and S. Tourtounis. On labeling schemes for the semantic web. In WWW, pages 544–555, 2003.
  12. W. W. Cohen, R. E. Schapire, and Y. Singer. Learning to order things. J. Artif. Intell. Res. (JAIR), 10:243– 270, 1999.
  13. R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. In PODS, pages 102– 113, 2001.
  14. P. Georgiadis, I. Kapantaidakis, V. Christophides, E. M. Nguer, and N. Spyratos. Efficient rewriting algorithms for preference queries. In ICDE, pages 1101–1110, 2008.
  15. S. Holland, M. Ester, and W. Kießling. Preference mining: A novel approach on mining user preferences for personalized applications. In PKDD, pages 204–216, 2003.
  16. I. F. Ilyas, W. G. Aref, and A. K. Elmagarmid. Supporting top-k join queries in relational databases. In VLDB, pages 754–765, 2003.
  17. T. Joachims. Optimizing search engines using clickthrough data. In KDD, pages 133–142, 2002
  18. W. Kießling. Foundations of preferences in database systems. In VLDB, pages 311–322, 2002.
  19. W. Kießling and G. Kostler. Preference SQL - design, implementation, experiences. In VLDB, pages 990– 1001, 2002.
  20. G. Koutrika and Y. E. Ioannidis. Personalization of queries in database systems. In ICDE, pages 597–608, 2004
  21. M. Lacroix and P. Lavency. Preferences: Putting more knowledge into queries. In VLDB, pages 217–225, 1987.
  22. J. Levandoski, M. Mokbel, and M. Khalefa. FlexPref: A framework for extensible preference evaluation in database systems. In ICDE, pages 828–839, 2010.
  23. C. Li, K. C.-C. Chang, I. F. Ilyas, and S. Song. RankSQL: Query algebra and optimization for relational topk queries. In SIGMOD, pages 131–142, 2005.
  24. C. Mishra and N. Koudas. Stretch 'n' shrink: Resizing queries to user preferences. In SIGMOD, pages 1227– 1230, 2008.
  25. P. G. Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, and T. G. Price. Access path selection in a relational database management system. In SIGMOD, pages 23–34, 1979.
  26. K. Stefanidis, E. Pitoura, and P. Vassiliadis. Adding context to preferences. In ICDE, pages 846–855, 2007.
Back