A Keyword Query Method Based on Digital Type Properties

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Abstract:

Now, the way to access the database in most still uses a simple keyword query method, it is generally based on semantic matching, rather than based on semantic matching. So it can't make full use of the semantic relationship between the data, and unable to obtain accurate results. This paper proposes a keyword query method based on digital type, it can largely improve the efficiency of user queries. We propose two query algorithm namely multi-table query and single table query in the database query time of uncertainty. Through experimental analysis, we can see that our algorithm is more effective than simple type keyword queries.

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Periodical:

Advanced Materials Research (Volumes 919-921)

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2115-2118

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Online since:

April 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Lin Zi-yu, Yang Dong-qing, Wang Teng-jiao, and Zhang Dong-zhan, Key word search over relational databases, Journal of Software, vol. 21(10), pp.2454-2476, October, (2010).

Google Scholar

[2] Zhou Fan, Li Shu-quan, Xiao Chun-jing, and Wu Yue, Probabilistic top-k and ranking query algorithms in uncertain databases, Journal of Computer Applications, vol. 30(10), pp.2605-2609, October, (2010).

DOI: 10.3724/sp.j.1087.2010.02605

Google Scholar

[3] Zhang Zheng, Yang Wei-dong, and Zhu Hao, Top-k keyword query on uncertain database, Journal of Frontiers of Computer Science and Technology, vol. 5(9), pp.781-790, September, (2011).

Google Scholar

[4] Soliman M A, Ilyas I F, and Chang K C-C. Top-k query processing in uncertain databases, Proceedings of the 23rd International Conference on Data Engineering (ICDE), Istanbul, Turkey, 2007, pp.896-905.

DOI: 10.1109/icde.2007.367935

Google Scholar

[5] X. Zhang, J. Chomicki. Semantics and evaluation of top-k queries in probabilistic databases. Distributed and Parallel Databases. 2009, 26(1): 67−126.

DOI: 10.1007/s10619-009-7050-y

Google Scholar