Back to Journal

Home»Articles»Design and Development of XML Data Salvage with Semantic Scrutiny and Query Enlargement

JournalCover

Asian Journal of Science and Applied Technology (AJSAT)

Editor Dr. Balaji Ramachandran
Print ISSN : 2249 - 0698
Frequency : Half-yearly

Design and Development of XML Data Salvage with Semantic Scrutiny and Query Enlargement

Author : S. Ravichandran and J. Sunitha John
Volume 9 No.1 January-June 2020 pp 22-27

Abstract

Databases are utilized to keep up information esteems in an organized way. Information and its depiction subtleties are kept up in a tri organized way in xml report. XPath and XQuery inquiry dialects are utilized to question XML information. XQuery is genuinely confused to comprehend its structure. Inquiry dialects require the information about the record pattern. Watchword based inquiry models does not requires the earlier information about the XML record structure.  XML report recovery is performed with catchphrase-based question. In catchphrase question model hunt inquiry watchword is passed to the framework to bring the significant archives. Fluffy sort ahead inquiry in XML information conspire is applied to look XML reports with question catchphrase. Auto-complete and auto-adjustment strategies are utilized to submit question catchphrases. Record structures and looking through calculations are utilized to improve the quality and positioning procedure. Alter separation is utilized to evaluate the similitude between two words. Insignificant cost tree is built to file the watchwords. Definite hunt and fluffy pursuit procedures are applied to get records. The top-K results are brought from top-K pertinence strategy. Fluffy sort ahead search conspire is upgraded with idea investigation and question development strategies. List model is improved with watchword pertinence and weight esteems. The framework is upgraded with search history-based question help conspire. Weight edge-based recovery is given in the framework.

Keywords

GDMCT, Fuzzy Search, TASX, XML, ERCS, and Ontology.

Full Text:

References

  1. Y.Xu aPapakonstantinou, “Efficient LCA Based Keyword Search in XML Data,” Proc.  Int’l Conf.   Extending Database Technology: Advances in Database Technology   (EDBT), pp.  535-546,2008.
  2. S.Ji,G.Li,C.Li, and J.Feng, “EfficientInteractiveFuzzy Keyword Search,”Proc.Int’l CWorldWide Web(WWW),pp.371-380,2009
  3. R. Fagin, A. Lotem, and M.Naor, “Optimal Aggregation Algorithmsfor Middleware,” Proc. ACMSIGMOD- SIGACTSIGART Symp. Principles of Database Systems (PODS),2001.
  4. G.Koutrika, M.Zadeh, and H. Garcia-Molina,“Data Clouds: Summarizing Keyword Search Results over   Structured  Data,”  Proc.  Int’l Conf.   Extending Database Technology: Advances in Database Technology    (EDBT), pp.  391-402,2009.
  5. G.Li,Ji, C.Li, and J.Feng, “Efficient Type-Ahead Search on Relational Data:A Tastier Approach,” Proc.ACMSIGMODInt’l Conf. Management of Data,  pp. 695-706,2009.
  6. Z. Bao,T.Ling,B.Chen, and J. Lu,“Effective XMLKeyword Searchwith RelevanceOrientedRanking,” Proc. Int’l Conf. Data Eng. (ICDE),2009.
  7. G.Li,Li, J.Feng, and L.Zhou, “Sail: Structure-Aware Indexing for Effective    and    Progressive    Top-k Keyword      Search      over      XML Documents,”   Information   Sciences, Vol.179, No.21, pp.3745-3762, 2009.
  8. G. Li, J. Feng, and L.  Zhou, “Interactive Search in  Xml  Data,” Proc. Int’l Conf. World WideWeb(WWW),pp.1063-1064,2009.
  9. Y. Chen, W.Wang, Z.Liu, and X. Lin, “Keyword Search on Structured and Semi-Structured Data,” P ACMSIGMOD Int’lConf. Management of Data, pp.1005-1010, 2009.
  10. E.  Chu, A.  Baid, X.  Chai, A. Doan, and J.F. Naughton,“Combining Keyword Search and Forms for AdHocQueryingof Databases,”P ACMSIGMOD Int’lConf. Management of Data, pp. 349-360,2009.
  11. Guo, F.Shao,C. Botev, and J. Shanmugasundaram, “Xrank: Ranked Keyword Search over Xml Documents,” Proc.  ACM SIGMOD Int’l Conf. Management of Data, pp.16-27, 2003.
  12. Srikanthand A. G. Ramakrishna, ―Contextual encoding in uniform and adaptive mesh-based lossless compressionofMRimages,‖IEEETrans.Med.Imag.,Vol.24,No.9, pp.1199–1206,Sep.2005.
  13. Menegaz andJ.P.Thirian, “Three-dimensional encoding/two-dimensional decoding of medical data”, ‖IEEE Trans.Med.Imag.,Vol.22, No.3, pp.424-440,Mar.2003.
  14. Doukas and I.Maglogiannis, “Regionofinterest coding techniques for medical image compression,”  IEEE Eng.Med.Biol.Mag.,Vol.25, No.5,pp.29–35,Sep.–Oct. 2007.
  15. SaidandW.Pearlman,―A new fast and efficient image coded based onset partitioning in hierarchical trees, IEEETrans.CircuitsSyst.VideoTechnol.,Vol.6,No.3,pp. 243–250,Jun.1996.
  16. Taubman, “High performance scalable image compression with EBCOT”, IEEETrans .Image Process., Vol.9,No.7,pp.1158–1170,Jul.2000.
  17. Qin, J.X.Yu, and L. Chang, “Keyword Searchin Databases: The Power of RDBMS,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp.681-694, 2009.
The Research Publication