Towards introducing and verifying heterogeneous data integration model for cloud

2019 ◽  
Author(s):  
Deepali Tripathi ◽  
N.K. Joshi
2014 ◽  
Vol 912-914 ◽  
pp. 1201-1204
Author(s):  
Gang Huang ◽  
Xiu Ying Wu ◽  
Man Yuan

This paper provides an ontology-based distributed heterogeneous data integration framework (ODHDIF). The framework resolves the problem of semantic interoperability between heterogeneous data sources in semantic level. By metadatas specifying the distributed, heterogeneous data and by describing semantic information of data source , having "ontology" as a common semantic model, semantic match is established through ontology mapping between heterogeneous data sources and semantic difference institutions are shielded, so that semantic heterogeneity problem of the heterogeneous data sources can be effectively solved. It provides an effective technology measure for the interior information of enterprises to be shared in time accurately.


2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Li Jiang ◽  
Hao Chen ◽  
Yueqi Ouyang ◽  
Canbing Li

With the rapid development of information technology and the coming of the era of big data, various data are constantly emerging and present the characteristics of autonomy and heterogeneity. How to optimize data quality and evaluate the effect has become a challenging problem. Firstly, a heterogeneous data integration model based on retrospective audit is proposed to locate the original data source and match the data. Secondly, in order to improve the integrated data quality, a retrospective audit model and associative audit rules are proposed to fix incomplete and incorrect data from multiple heterogeneous data sources. The heterogeneous data integration model based on retrospective audit is divided into four modules including original heterogeneous data, data structure, data processing, and data retrospective audit. At last, some assessment criteria such as redundancy, sparsity, and accuracy are defined to evaluate the effect of the optimized data quality. Experimental results show that the quality of the integrated data is significantly higher than the quality of the original data.


2010 ◽  
Vol 11 (3) ◽  
pp. 292-298
Author(s):  
Hongjun SU ◽  
Yehua SHENG ◽  
Yongning WEN ◽  
Min CHEN

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