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Frontiers of Computer Science

Front. Comput. Sci.    2018, Vol. 12 Issue (6) : 1125-1139     https://doi.org/10.1007/s11704-016-6319-3
RESEARCH ARTICLE |
Automatic Web-based relational data imputation
Hailong LIU(), Zhanhuai LI, Qun CHEN, Zhaoqiang CHEN
School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
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Abstract

Data incompleteness is one of the most important data quality problems in enterprise information systems. Most existing data imputing techniques just deduce approximate values for the incomplete attributes by means of some specific data quality rules or some mathematical methods. Unfortunately, approximationmay be far away from the truth. Furthermore, when observed data is inadequate, they will not work well. The World Wide Web (WWW) has become the most important and the most widely used information source. Several current works have proven that using Web data can augment the quality of databases. In this paper, we propose a Web-based relational data imputing framework, which tries to automatically retrieve real values from the WWW for the incomplete attributes. In the paper, we try to take full advantage of relations among different kinds of objects based on the idea that the same kind of things must have the same kind of relations with their relatives in a specific world. Our proposed techniques consist of two automatic query formulation algorithms and one graph-based candidates extraction model. Several evaluations are proposed on two high-quality real datasets and one poor-quality real dataset to prove the effectiveness of our approaches.

Keywords data incompleteness      imputation      World Wide Web      query formulation      candidate selection      semantic relation     
Corresponding Authors: Hailong LIU   
Just Accepted Date: 23 December 2016   Online First Date: 06 March 2018    Issue Date: 04 December 2018
 Cite this article:   
Hailong LIU,Zhanhuai LI,Qun CHEN, et al. Automatic Web-based relational data imputation[J]. Front. Comput. Sci., 2018, 12(6): 1125-1139.
 URL:  
http://journal.hep.com.cn/fcs/EN/10.1007/s11704-016-6319-3
http://journal.hep.com.cn/fcs/EN/Y2018/V12/I6/1125
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Hailong LIU
Zhanhuai LI
Qun CHEN
Zhaoqiang CHEN
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