European Journal of Scientific Research - EuroJournals

More documents

Recommendations

Info

850 Imran Sarwar Bajwa, Shahzad Mumtaz and M. Shahid Naweed Table 04: Accuracy ratio of various types of queries Types Simple Complex Total SELECT 14 13 90% INSERT 13 11 80% DELETE 14 12 87% Total Accuracy = 86% A matrix representing accuracy of query generation test (%) for simple level and complex level queries has been constructed. Overall diagrams accuracy for all types of queries is determined by adding total accuracy of all categories and calculating its average that is 86% in this case. Figure 05: Graphical representation of the results 14 12 10 8 6 4 2 0 SELECT INSERT DELETE The graph above is showing the accuracy ratio of various SELECT, INSERT & DELETE queries in terms of simple and complex queries parameters. 6.0. Conclusion The designed system “Computational Linguistics based System for Automatic Database Query Generation” facilitates both users and software engineers in terms of generating SQL queries automatically. The task of the novel user can be simplified by providing an easy interface that is more familiar and well known to that user. In order to resolve all such issues, an automated software is needed, which facilitates both users and software engineers. User writes the requirements in simple English in a few statements and the designed system has obvious ability to analyze the given script. After composite analysis and mining of associated information, the designed system generates the intended SQL queries that can be run directly. The designed system has robust ability to create code automatically without external environment. The designed system provides a quick and reliable way to generate SQL queries to save the time and budget of both the user and system analyst. An elegant graphical user interface has also been provided to the user for entering the Input scenario in a proper way and generating UML diagrams. 7.0. Future Work There is also some margin of improvements in the algorithms for generating the intended SQL queries. Current accuracy of generating diagrams is about 80% to 85%. It can be enhanced up to 95% by improving the algorithms and inducing the ability of learning in the system. In this research only three types of queries has been addressed as SELECT, INSERT, and DELETE query. There are still other types of queries that require some sufficient solution. Simple Complex
Database Interfacing using Natural Language Processing 851 References [1] Allen,J. (1994) Natural Language Understanding. Benjamin- Cummings Publishing Company, New York. [2] Biber, D., Conrad, S., & Reppen, R. (1998). Corpus Linguistics: Investigating Language Structure and Use. Cambridge Univ. Press, Cambridge, U.K. [3] D. DeHaan, D. Toman, M. P. Consens, and T. Ozsu. (2003) A Comprehensive XQuery to SQL Translation using Dynamic Interval Encoding. In SIGMOD. [4] C. A. Thompson, R. J. Mooney and L. R. Tang, Learning to parse natural language database queries into logical form, in: Workshop on Automata Induction, Grammatical Inference and Language Acquisition (1997). [5] Salton, G., & McGill, M. (1983). Introduction to Modern Information Retrieval. McGraw-Hill, New York. [6] A. Rosenthal. D. Reiner, Extending the Algebraic Framework of Query Processing to Handle Outer joins, Proc. VLDB Singa- pore 1984. pp. 334-343. [7] Fagan, J. L. (1989). The effectiveness of a non-syntactic approach to automatic phrase indexing for document retrieval. Journal of the American Society for Information Science, 40 (2), 115– 132. [8] J. M. Zelle and R. J. Mooney, Learning semantic grammars with constructive inductive logic programming, in: Proceedings of the 11th National Conference on Arti_cial Intelligence (AAAI Press/MIT Press, Washington, D.C., 1993), pp. 817ñ822. [9] Kowalski, G. (1998). Information Retrieval Systems: Theory and Implementation. Kluwer, Boston. [10] Krovetz, R., & Croft, W. B. (1992). Lexical ambiguity and information retrieval. ACM Transactions on Information Systems, 10, 115–141. [11] Losee, R. M. (1988). Parameter estimation for probabilistic document retrieval models. Journal of the American Society for Information Science, 39(1), 8–16. [12] Losee, R. M. (1996a). Learning syntactic rules and tags with genetic algorithms for information retrieval and filtering: An empirical basis for grammatical rules. Information Processing and Management, 32(2), 185–197. [13] Manning, C. D., & Schutze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, Mass. [14] Partee, B. H., Meulen, A. t., &Wall, R. E. (1990). Mathematical Methods in Linguistics. Kluwer, Dordrecht, The Netherlands.
Page 1 and 2:
European Journal of Scientific Rese
Page 3 and 4:
appropriate. Manuscripts may be rej
Page 5:
Database Interfacing using Natural
Page 8 and 9:
Blast-Hole Cuttings: An Indicator o
Page 10 and 11:
Page 12 and 13:
Page 14 and 15:
Page 16 and 17:
Page 18 and 19:
Page 20 and 21:
Page 22 and 23:
Page 24 and 25:
Assessment of Micro-Credit Supply b
Page 26 and 27:
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
748 Muhammad Nawaz Mahsud, Muhammad
Page 36 and 37:
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
Page 46 and 47:
Design Space Exploration of Regular
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
Page 56 and 57:
Page 58 and 59:
Page 60 and 61:
774 Kouamé kan Jean, Jourda Jean P
Page 62 and 63:
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
790 F.R. Falayi, A.S. Ogunlowo and
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
798 S. Gherbi, S. Yahmedi and M. Se
Page 86 and 87: 800 S. Gherbi, S. Yahmedi and M. Se
Page 92 and 93: Effects of Enviromental Variables o
Page 104 and 105: Spatial Distribution and Characteri
Page 114 and 115: European Journal of Scientific Rese
Page 116 and 117: 830 Taba Mohamed Tahar, S. Femmame
Page 124 and 125: 838 Dembele Ardjouma, Oumarou Badin
Page 132 and 133: 846 Imran Sarwar Bajwa, Shahzad Mum
Page 134 and 135: 848 Imran Sarwar Bajwa, Shahzad Mum
Page 140 and 141: 854 Arshad Hasan and Safdar Butt te
Page 142 and 143: 856 Arshad Hasan and Safdar Butt st
Page 144 and 145: 858 Arshad Hasan and Safdar Butt Ta
Page 146 and 147: 860 Arshad Hasan and Safdar Butt Pl
Page 148 and 149: 862 Arshad Hasan and Safdar Butt [2
Page 150 and 151: Development of Mechanical Prostheti
Page 158 and 159: Toxicity of Arsenic in the Ground W
Page 170 and 171: 884 Faten Semadi, Vincent Valles an
Page 178 and 179: A Predictive Current Control Techni
Page 186 and 187:
A Predictive Current Control Techni
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Effects of Ethyl acetate Portion of
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
916 Basil M. Kasasbeh, Rafa E. Al-Q
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
926 Samuel N. Ndubisi and Marcel .U
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
show all

European Journal of Scientific Research - EuroJournals

Create successful ePaper yourself

Delete template?

Save as template?