Improving the Performance of Keyword Search over Relational Database

International Journal of Computer Science and Engineering

© 2019 by SSRG - IJCSE Journal

Volume 6 Issue 12

Year of Publication : 2019

Authors : P. Sathishkumar, Dr. M. Gunasekaran

10.14445/23488387/IJCSE-V6I12P102

How to Cite?

P. Sathishkumar, Dr. M. Gunasekaran, "Improving the Performance of Keyword Search over Relational Database," SSRG International Journal of Computer Science and Engineering , vol. 6,  no. 12, pp. 10-14, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I12P102

Abstract:

The necessity of relational databases grows very larger .Relational database is structured to recognize relations between stored items of information. Extending the keyword search to relational data has been an active area of research. Many techniques have been proposed but all those techniques suffer from lack of standardization. Lack of standardization results in contradictory results. Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. It would be useful to identify queries that are
likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we analyze the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, In summary, our work confirms previous claims regarding the unacceptable performance of these search techniques and underscores the need for standardization in evaluations
 

Keywords:

Keyword search, relational, database,Graph.
 

References:

[1] D. Fallows, ―Search Engine Use,‖ technical report, Pew Internet and Am. Life Project, http://www.pewinternet.org/Reports/ 2008/Search-
Engine-Use.aspx. Aug. 2008.
[2] comScore, ―Global Search Market Grows 46 Percent in2009,‖ Global_Search Market_Grows_46_%_in_2009, Jan. 2010.
[3] J. Coffman and A.C. Weaver, ―A Framework for Evaluating Database Keyword Search Strategies,‖ Proc. 19th ACM Int’l Conf. Information and
Knowledge Management (CIKM ’10), pp. 729-738, Oct. 2010.
[4] Y. Chen, W. Wang, Z. Liu, and X. Lin, ―KeywordSearch on Structured and Semi-Structured Data,‖ Proc. ACM SIGMOD Int’l Conf. Management of
Data (SIGMOD ’09), pp. 1005-1010, June 2009.
[5] W. Webber, ―Evaluating the Effectiveness of Keyword Search,‖IEEE Data Eng. Bull., vol. 33, no. 1, pp. 54-59, Mar. 2010.
[6] A. Baid, I. Rae, J. Li, A. Doan, and J. Naughton, ―Toward Scalable Keyword Search over Relational Data,‖ Proc. VLDB Endowment, vol. 3, no. 1, pp.
140-149, 2010.
[7] Q. Su and J. Widom, ―Indexing Relational Database Content Offline for Efficient Keyword- Based Search,‖ Proc. Ninth Int’l Database Eng. and
Application Symp. (IDEAS ’05), pp. 297-306, July 2005.
[8] V. Kacholia, S. Pandit, S. Chakrabarti, S. Sudarshan, R. Desai, and H. Karambelkar, ―Bidirectional Expansion For Keyword Search on Graph Databases,‖ Proc. 31st Int’l Conf. Very Large Data Bases (VLDB’05), pp. 505-516, Aug. 2005.
[9] H. He, H. Wang, J. Yang, and P.S. Yu, ―BLINKS: Ranked Keyword Searches on Graphs,‖ Proc. ACM SIGMOD Int’l Conf. Management of Data (SIGMOD ’07), pp. 305-316, June 2007.
[10] G. Kasneci, M. Ramanath, M. Sozio, F.M. Suchanek, and G. Weikum, ―STAR: Steiner-Tree Approximation in Relationship Graphs,‖ Proc. Int’l
Conf. Data Eng. (ICDE ’09), pp. 868-879, Mar. 2009.
[11] G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan, ―Keyword Searching and Browsing in Databases Using BANKS,‖ Proc. 18th
Int’l Conf. Data Eng. (ICDE ’02), pp. 431-440, Feb. 2002.
[12] B. Ding, J.X. Yu, S. Wang, L. Qin, X. Zhang, and X. Lin, ―Finding Top-k Min-Cost Connected Trees in Databases,‖ Proc. 23rd Int’l Conf. Data Eng. (ICDE ’07), pp. 836-845, Apr. 2007.
[13] G. Li, B.C. Ooi, J. Feng, J. Wang, and L. Zhou, ―EASE: An Effective 3-in-1 Keyword Search Method for Unstructured, Semi-Structured and Structured Data,‖ Proc. ACM SIGMOD Int’l Conf. Management of Data (SIGMOD ’08), pp. 903-914, June 2008.
[14] L. Qin, J. Yu, L. Chang, and Y. Tao, ―Querying Communities in Relational Databases,‖ Proc. IEEE Int’l Conf. Data Eng. (ICDE ’09), pp. 724-735, Mar. 2009.
[15] G. Li, J. Feng, X. Zhou, and J. Wang, ―Providing Built-in Keyword Search Capabilities in RDBMS,‖ The VLDB J., vol. 20, pp. 1-19, Feb. 2011.
[16] V. Hristidis and Y. Papakonstantinou, ―DISCOVER: Keyword Search in Relational Databases,‖ Proc. 28th Int’l Conf. Very Large Data Base (VLDB ’02), pp. 670-681, Aug. 2002.
[17] V. Hristidis, L. Gravano, and Y. Papakonstantinou, ―Efficient IR- Style Keyword Search over Relational Databases,‖ Proc. 29th Int’l Conf. Very Large Data Bases (VLDB ’03), pp. 850-861, Sept. 2003
[18] A. Singhal, J. Choi, D. Hindle, D. Lewis, and F. Pereira, ―AT&T at TREC-7,‖ Proc. Seventh Text REtrieval Conf. (TREC-7), pp. 239-252, Nov. 1999.
[19] S.E. Dreyfus and R.A. Wagner, ―The Steiner Problem in Graphs,‖ Networks, vol. 1, no. 3, pp. 195-207, 1971.
[20] G. Reich and P. Widmayer, ―Beyond Steiner’s Problem: A VLSI Oriented Generalization,‖ Proc. 15th Int’l Workshop Graph-Theoretic Concepts in
Computer Science, pp. 196-210, 1990.