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Research on Hybrid Data Verification Method for Educational Data

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Data Science (ICPCSEE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 901))

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Abstract

With the development of educational informatization, the problem of data quality has become the main problem that restricts the development of educational informatization. It is particularly important to manage and improve the data quality at the life cycle of the data by intelligent means. This paper presents a data verification framework orienting education data based on rule base, and gives the hybrid data verification method. The method is applied to the education statistics foundation database platform in the data verification process, and improves the precision of data.

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References

  1. Han, C.-G.: Data verification based on knowledge base. Beijing University of Technology (2003)

    Google Scholar 

  2. Xiao, M.: Implementation of data checking based on the rule in database application system. Sci. Technol. Inf. (21), 337–339 (2007)

    Google Scholar 

  3. Su, X., Shen, Z., Liu, N.: Excel data quality checking tool based on knowledge rules. E-Sci. Technol. Appl. 3(03), 29–37 (2012)

    Google Scholar 

  4. Lin, X., Shen, D., Shi, Y., Qiao, D.: Configurable data checking method. Comput. Syst. Appl. 24(05), 161–166 (2015)

    Google Scholar 

  5. Huang, H.: A data quality detection method based on multi-dimensional check rules. Shandong CN106528828A 22, March 2017

    Google Scholar 

  6. Xu, H., Wang, J., Wu, L., Sun, R., Li, L.: Research and application of multi-dimension check for running data of new energy unit. N. China Electr. Power (10), 7–12+18 (2017)

    Google Scholar 

  7. Wang, T.: Discussion on data checking mechanism of rule-based power dispatching plan. Electr. Prod. (12), 263 (2015)

    Google Scholar 

  8. Liu, Y., Shen, X., Xu, L., et al.: A MapReduce based parallel algorithm for CIM data verification. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 704–709. IEEE (2014)

    Google Scholar 

  9. Liu, B., Gen, Y.: The mining method of data quality detecting rules. Pattern Recogn. Artif. Intell. 25(05), 835–844 (2012)

    Google Scholar 

  10. Ebaid, A., Elmagarmid, A., Ilyas, I.F., et al.: NADEEF: a generalized data cleaning system. Proc. VLDB Endow. 6(12), 1218–1221 (2013)

    Article  Google Scholar 

  11. Liu, C., Zhang, K., Chen, J.: Research on data validation scheme of mixed mode. Comput. Eng. Des. 34(01), 366–371 (2013)

    Google Scholar 

  12. Zhou, Y.: Research and application of data cleaning algorithm. Qiingdao University (2005)

    Google Scholar 

  13. Sheng, X., Hun, S.: Similar duplicate records elimination based on improved SNM algorithm. J. Chongqing Univ. Technol. (Nat. Sci.) 30(04), 91–96 (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Xi’an University of Technology, Xi’an, 710048, China

    Lin Dong, Xinhong Hei, Ping He & Bin Wang

  2. Xi’an Educational Television, Xi’an, 710001, China

    Xiaojiao Liu

Authors
  1. Lin Dong
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  2. Xinhong Hei
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  3. Xiaojiao Liu
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  4. Ping He
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  5. Bin Wang
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Corresponding author

Correspondence to Lin Dong .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, Henan, China

    Qinglei Zhou

  2. Zhengzhou University of Light Industry, Zhengzhou, Henan, China

    Yong Gan

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. Zhengzhou Institute of Technology, Zhengzhou, China

    Yan Wang

  6. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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© 2018 Springer Nature Singapore Pte Ltd.

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Dong, L., Hei, X., Liu, X., He, P., Wang, B. (2018). Research on Hybrid Data Verification Method for Educational Data. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_6

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  • DOI: https://doi.org/10.1007/978-981-13-2203-7_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2202-0

  • Online ISBN: 978-981-13-2203-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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