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Open AccessArticle

Processing on Structural Data Faultage in Data Fusion

by Fan Chen 1, Ruoqi Hu 2, Jiaoxiong Xia 1,2,3,* and Jie Tao 2
School of Computer Engineering and Science, Shanghai University, Shangda Road 99, Shanghai 200444, China
XianDa College of Economics and Humanities, Shanghai International Studies University, East Tiyuhui Road 390, Shanghai 200083, China
Information Centre, Shanghai Municipal Education Commission, Dagu Road 100, Shanghai 200003, China
Author to whom correspondence should be addressed.
Received: 30 December 2019 / Revised: 22 February 2020 / Accepted: 3 March 2020 / Published: 6 March 2020
With the rapid development of information technology, the development of information management system leads to the generation of heterogeneous data. The process of data fusion will inevitably lead to such problems as missing data, data conflict, data inconsistency and so on. We provide a new perspective that combines the theory in geology to conclude such kind of data errors as structural data faultage. Structural data faultages after data integration often lead to inconsistent data resources and inaccurate data information. In order to solve such problems, this article starts from the attributes of data. We come up with a new solution to process structural data faultages based on attribute similarity. We use the relation of similarity to define three new operations: Attribute cementation, Attribute addition, and Isomorphous homonuclear. Isomorphous homonuclear uses digraph to combine attributes. These three operations are mainly used to handle multiple data errors caused by data faultages, so that the redundancy of data can be reduced, and the consistency of data after integration can be ensured. Finally, it can eliminate the structural data faultage in data fusion. The experiment uses the data of doctoral dissertation in Shanghai University. Three types of dissertation data tables are fused. In addition, the structural data faultages after fusion are processed by the new method proposed by us. Through the statistical analysis of the experiment results and compare with the existing algorithm, we verify the validity and accuracy of this method to process structural data faultages. View Full-Text
Keywords: data fusion; structural data faultage; isomorphous homonuclear; information entropy; data structure integrity data fusion; structural data faultage; isomorphous homonuclear; information entropy; data structure integrity
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Chen, F.; Hu, R.; Xia, J.; Tao, J. Processing on Structural Data Faultage in Data Fusion. Data 2020, 5, 21.

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