Query Based System for Data Retrieval
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Abstract
Query Based System (QBS) divide into three parts pre-processing, query processing and post processing. In the pre -processing end user will give the query input then query will break down in small queries then auto correction will be there and obtain the result in query processing. At last in post processing analyze the result combine the result and return the result to the user. A dynamic database query handler for databases of different size as well as different table structures. The database handling becomes usually a big issue for the small enterprises which are not able to afford the cost of the database designer and administrators.
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Cheung, Alvin, Armando Solar-Lezama, and Samuel Madden. "Optimizing database-backed applications with query synthesis." ACM SIGPLAN Notices48, no. 6 (2013): 3-14.
Le, Wangchao, Anastasios Kementsietsidis, Songyun Duan, and Feifei Li. "Scalable multi-query optimization for SPARQL." In Data Engineering (ICDE), 2012 IEEE 28th International Conference on, pp. 666-677. IEEE, 2012.
Llopis, Miguel, and Antonio Ferrández. "How to make a natural language interface to query databases accessible to everyone: An example." Computer Standards & Interfaces 35, no. 5 (2013): 470-481.
Gottlob, Georg, Giorgio Orsi, and Andreas Pieris. "Ontological queries: Rewriting and optimization." In Data Engineering (ICDE), 2011 IEEE 27th International Conference on, pp. 2-13. IEEE, 2011.
Abouzied, Azza, Joseph Hellerstein, and Avi Silberschatz. "Dataplay: interactive tweaking and example-driven correction of graphical database queries." In Proceedings of the 25th annual ACM symposium on User interface software and technology, pp. 207-218. ACM, 2012.
Franklin, Michael J., Donald Kossmann, Tim Kraska, Sukriti Ramesh, and Reynold Xin. "CrowdDB: answering queries with crowdsourcing." InProceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp. 61-72. ACM, 2011.
Zhang, Sai, and Yuyin Sun. "Automatically synthesizing SQL queries from input-output examples." In Automated Software Engineering (ASE), 2013 IEEE/ACM 28th International Conference on, pp. 224-234. IEEE, 2013.
Basin, David, Manuel Clavel, Marina Egea, M. A. Garcia de Dios, and Carolina Dania. "A model-driven methodology for developing secure data-management applications." (2014): 1-1.
M. J. Carey and D. Kossmann. Reducing the Braking Distance of an SQL Query Engine. In VLDB, pages 158–169, 1998.
I. F. Ilyas, W. G. Aref, and A. K. Elmagarmid. Supporting Top-k Join Queries in Relational Databases. In VLDB, pages 754–765, 2003.
Z. G. Ives, D. Florescu, M. Friedman, A. Y. Levy, and D. S. Weld. An Adaptive Query Execution System for Data Integration. In SIGMOD 1999, pages 299–310, 1999.
C. Jermaine, A. Pol, and S. Arumugam. Online maintenance of very large random samples. In SIGMOD, pages 299–310, 2004.
D. T. Liu and M. J. Franklin. GridDB: A Data-Centric Overlay for Scientific Grids. In VLDB, pages 600–611, 2004.
G. Luo, C. J. Ellmann, P. J. Haas, and J. F. Naughton. A scalable hash ripple join algorithm. In SIGMOD, pages 252– 262, 2002.
G. Luo, J. F. Naughton, C. Ellmann, and M. Watzke. Toward a progress indicator for database queries. In SIGMOD, pages 791–802, 2004