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CrowdCleaner: A Data Cleaning System Based on Crowdsourcing

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Web Technologies and Applications (APWeb 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8709))

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Abstract

As data in real life is often dirty, data cleaning is a natural way to improve the data quality. However, due to the lack of human knowledge, existing automatic data cleaning systems cannot find the proper values for dirty data. Thus we propose an online data cleaning system CrowdCleaner based on Crowdsourcing. CrowdCleaner provides a friendly interface for users dealing with different data quality problems. In this demonstration, we show the architecture of CrowdCleaner and highlight a few of its key features. We will show the process of the CrowdCleaner to clean data.

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References

  1. Howe, J.: The rise of crowdsourcing. Wired Magazine 14(6), 1–4 (2006)

    MathSciNet  Google Scholar 

  2. Jin, L., Wang, H., Gao, H.: Imputation for categorical attributes with probabilistic reasoning. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds.) WAIM 2013. LNCS, vol. 7923, pp. 87–98. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  3. Galhardas, H., Florescu, D., Shasha, D., Simon, E., Saita, C.-A.: Declarative data cleaning: Language, model, and algorithms. In: VLDB, pp. 371–380 (2001)

    Google Scholar 

  4. Raman, V., Hellerstein, J.M.: Potter’s wheel: An interactive data cleaning system. In: VLDB, pp. 381–390 (2001)

    Google Scholar 

  5. Redman, T.C.: Data: An unfolding quality disaster. Information Management Magazine (August 2004)

    Google Scholar 

  6. Shilakes, C., Tylman, J.: Enterprise information portals. Merrill Lynch (1998)

    Google Scholar 

  7. Bhattacharya, I., Getoor, L.: Collective entity resolution in relational data. ACM Transactions on Knowledge Discovery from Data (TKDD) 1(1), 5 (2007)

    Article  Google Scholar 

  8. Ye, C., Wang, H., Gao, H., Li, J., Xie, H.: Truth discovery based on crowdsourcing. In: Li, F., Li, G., Hwang, S.-w., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 453–458. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  9. Fan, W.: Dependencies revisited for improving data quality. In: PODS, pp. 159–170 (2008)

    Google Scholar 

  10. Cong, G., Fan, W., Geerts, F., Jia, X., Ma, S.: Improving Data Quality: Consistency and Accuracy. In: VLDB 2007, pp. 315–326 (2007)

    Google Scholar 

  11. Liu, S., Liu, Y., Ni, L.M., et al.: Towards mobility-based clustering. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 919–928. ACM (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, Harbin Institute of Technology, Harbin, 150000, China

    Chen Ye, Hongzhi Wang, Keli Li, Qian Chen, Jianhua Chen, Jiangduo Song & Weidong Yuan

Authors
  1. Chen Ye
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  2. Hongzhi Wang
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  3. Keli Li
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  4. Qian Chen
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  5. Jianhua Chen
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  6. Jiangduo Song
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  7. Weidong Yuan
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Editor information

Editors and Affiliations

  1. Beijing Institute of Spacecraft System Engineering, Beijing, China

    Lei Chen

  2. School of Computer Science, National University of Defense Technology, 410073, Changsha, Hunan, China

    Yan Jia

  3. RMIT University, Melbourne, Australia

    Timos Sellis

  4. School of Computer Science and Technology, Soochow University, 215006, Suzhou, China

    Guanfeng Liu

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© 2014 Springer International Publishing Switzerland

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Cite this paper

Ye, C. et al. (2014). CrowdCleaner: A Data Cleaning System Based on Crowdsourcing. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_64

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  • DOI: https://doi.org/10.1007/978-3-319-11116-2_64

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11115-5

  • Online ISBN: 978-3-319-11116-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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