scholarly journals A Multisource Retrospective Audit Method for Data Quality Optimization and Evaluation

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Li Jiang ◽  
Hao Chen ◽  
Yueqi Ouyang ◽  
Canbing Li
Keyword(s):  
Data Integration ◽  
Data Quality ◽  
Rapid Development ◽  
Original Data ◽  
Heterogeneous Data ◽  
Integration Model ◽  
Heterogeneous Data Integration ◽  
Model Based ◽  
Retrospective Audit

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.

Design and Implementation of Oilfield Heterogeneous Data Integration Model Based on Ontology

2014 ◽  
Vol 912-914 ◽  
pp. 1201-1204
Author(s):  
Gang Huang ◽  
Xiu Ying Wu ◽  
Man Yuan
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Semantic Heterogeneity ◽  
Integration Model ◽  
Integration Framework ◽  
Heterogeneous Data Integration ◽  
Semantic Level ◽  
Heterogeneous Data Sources ◽  
Semantic Difference

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.

Synergy Network Inference Model Based on Heterogeneous Data Integration

2018 ◽  
Vol 24 (2) ◽  
pp. 1076-1079
Author(s):  
Farzana Kabir Ahmad ◽  
Siti Sakira Kamaruddin ◽  
Yuhanis Yusof ◽  
Nooraini Yusoff
Keyword(s):  
Data Integration ◽  
Network Inference ◽  
Heterogeneous Data ◽  
Inference Model ◽  
Heterogeneous Data Integration ◽  
Model Based

A Method of Heterogeneous Data Integration Based on SOA

2014 ◽  
Vol 536-537 ◽  
pp. 494-498
Author(s):  
Wen Ming Shuai ◽  
Xiu Fen Fu
Keyword(s):  
Data Integration ◽  
Service Oriented Architecture ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Integration System ◽  
Heterogeneous Data Integration ◽  
Web Of Data ◽  
Data Integration System ◽  
Service Oriented ◽  
The Web

With the rapid development of information technology, the growth of heterogeneous Web data and the requirements of access to the Web of data also is growing. In view of this, a method of heterogeneous data integration based on SOA(Service-Oriented Architecture) is proposed. This method combines the technology of middleware and SOA design, using XML and Web services technologies, presents a framework of heterogeneous data integration based on SOA, and introduces the architecture of SOA data integration middleware. Experimental results show that this method reduces the coupling of heterogeneous data integration system effectively, and improves the scalability of the system.

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