General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Cecilia Xie
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • APC: 800 USD
    • E-mail: editor@ijcte.org
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IJCTE 2018 Vol.10(4): 121-124 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2018.V10.1211

A Novel Framework for Improvement of Data Quality on Integration

Zhang Guobao

Abstract—In the integrated situation of distributed, heterogeneous, and frequently changing data the improvement of data quality issue is more complicated and difficult. That is because there are some characteristics such as dynamic, heterogeneous, timeliness, inherent data quality etc. The issue of inherent data quality dimensions was discussed in many former literatures, but that of improvement of the integrated data quality was few. This paper proposed a framework of mastering data source, data catalog, data quality monitoring and quality visualization in order to help improve the quality of integrated data from the view of the whole systematization. The concept of data quality check point was presented and our proposed method can monitor data quality more efficiently. The functionality of framework and prototypes was presented and our approach of improvement was provided. At last our analysis was given and the proposed frame was useful and effective.

Index Terms—Master data maintenance, data integration, data quality.

Zhang Guobao is with the Hohai University, China (e-mail: 20020108@hhu.edu.cn).

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Cite:Zhang Guobao, "A Novel Framework for Improvement of Data Quality on Integration," International Journal of Computer Theory and Engineering vol. 10, no. 4, pp. 121-124, 2018.


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