Jahnavi Ratna Deepika, Dr. S. Shanthi


Order and Return The most relevant results may be the most common form of XML query processing. To work around this problem, we first suggest an elegant query framework to support rough queries across XML data. The solutions based on this framework do not have to accurately fulfill the wording of the query but may be based on attributes that can be inferred in the original query. However, the current proposals do not take the structures into account adequately, in addition they do not have the power to combine structures and contents neatly to answer relaxation queries. Within our solution, we classify the contract into two groups: class attribute points, statistical attribute points, and pattern of related methods in relation to similarity ratings for holding the class attribute and statistical attribute points. We continue to benefit from a comprehensive set of experiments to demonstrate the effectiveness of our proposed approach when it comes to accuracy and recall metrics. XML data cannot be queried in practical applications, because the hierarchical structure of XML documents may be heterogeneous, or any slight misunderstanding of the structure of the document can certainly increase the risk of unsatisfactory query formulation. This is really difficult, especially given the fact that such inquiries give empty solutions, although they are not aggregative errors. In addition, we design a polygonal diagram based on an idea to create and regulate the relaxation of the structure and develop an inefficient evaluation coefficient to assess the relative relationship to structures. We therefore create a new retrieval approach from top k that can intelligently create promising solutions in a contextual arrangement using the order scale.


Top-K; Query Relaxations; XML; Answer Score; Querying XML;


S. Amer-Yahia, N. Koudas, A. Marian, D. Srivastava, and D. Toman, “Structure and Content Scoring for XML,” in Proc. Int. Conf. Very Large Data Bases, 2005, pp. 361–372.

J. Lu, T. W. Ling, C. Chan, and T. Chen, “From region encoding to extended dewey: On efficient processing of XML twig pattern matching,” in Proc. Int. Conf. Very Large Data Bases, 2005, pp. 193–204.

B. Fazzinga, S. Flesca, and A. Pugliiese, “Top-k answers to fuzzy XPath queries,” in Proc. Int. Conf. Database Expert Syst. Appl., 2009, pp. 822–829.

F. Weigel, H. Meuss, K. U. Schulz, and F. Bry, “Content and structure in indexing and ranking XML,” in Proc. Int. Workshop Web Databases, 2004, pp. 67–72.

A. Termehchy and M. Winslett, “Using structural information in XML keyword search effectively,” ACM Trans. Database Syst., vol. 36, no. 1, pp. 1–45, 2011.

Jian Liu and D. L. Yan, “Answering Approximate Queries Over XML Data”, ieee transactions on fuzzy systems, vol. 24, no. 2, april 2016.

H. Mousavi and C. Zaniolo, “Fast and accurate computation of equi-depth histograms over data streams,” in Proc. Int. Conf. Extending Database Technol., 2011, pp. 69–80.

Full Text: PDF


  • There are currently no refbacks.

Copyright © 2012 - 2021, All rights reserved.|

Creative Commons License
International Journal of Innovative Technology and Research is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJITR , Permissions beyond the scope of this license may be available at