Skip to main content

CrowdAidRepair: A Crowd-Aided Interactive Data Repairing Method

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9642))

Included in the following conference series:

  • 3617 Accesses


Data repairing aims at discovering and correcting erroneous data in databases. Traditional methods relying on predefined quality rules to detect the conflict between data may fail to choose the right way to fix the detected conflict. Recent efforts turn to use the power of crowd in data repairing, but the crowd power has its own drawbacks such as high human intervention cost and inevitable low efficiency. In this paper, we propose a crowd-aided interactive data repairing method which takes the advantages of both rule-based method and crowd-based method. Particularly, we investigate the interaction between crowd-based repairing and rule-based repairing, and show that by doing crowd-based repairing to a small portion of values, we can greatly improve the repairing quality of the rule-based repairing method. Although we prove that the optimal interaction scheme using the least number of values for crowd-based repairing to maximize the imputation recall is not feasible to be achieved, still, our proposed solution identifies an efficient scheme through investigating the inconsistencies and the dependencies between values in the repairing process. Our empirical study on three data collections demonstrates the high repairing quality of CrowdAidRepair, as well as the efficiency of the generated interaction scheme over baselines.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Bohannon, P., Fan, W., Flaster, M., Rastogi, R.: A cost-based model and effective heuristic for repairing constraints by value modification. In: SIGMOD, pp. 143–154 (2005)

    Google Scholar 

  2. Cong, G., Fan, W., Geerts, F., Jia, X., Ma, S.: Improving data quality: consistency and accuracy. PVLDB, 315–326 (2007)

    Google Scholar 

  3. Dallachiesa, M., Ebaid, A., Eldawy, A., Elmagarmid, A., Ilyas, I.F., Ouzzani, M., Tang, N.: Nadeef: a commodity data cleaning system. In: SIGMOD, pp. 541–552 (2013)

    Google Scholar 

  4. Fan, W., Geerts, F., Jia, X., Kementsietsidis, A.: Conditional functional dependencies for capturing data inconsistencies. ACM Trans. Database Syst. (TODS) 33(2), 6 (2008)

    Article  Google Scholar 

  5. Fan, W., Li, J., Ma, S., Tang, N., Yu, W.: Towards certain fixes with editing rules and master data. PVLDB 3(1–2), 173–184 (2010)

    Google Scholar 

  6. Hua, W., Wang, Z., Wang, H., Zheng, K., Zhou, X.: Short text understanding through lexical-semantic analysis. In: International Conference on Data Engineering (ICDE) (2015)

    Google Scholar 

  7. Koh, J.L.Y., Li Lee, M., Hsu, W., Lam, K.T.: Correlation-based detection of attribute outliers. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 164–175. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  8. Kolahi, S., Lakshmanan, L.V.: On approximating optimum repairs for functional dependency violations. In: ICDT, pp. 53–62 (2009)

    Google Scholar 

  9. Lopatenko, A., Bravo, L.: Efficient approximation algorithms for repairing inconsistent databases. In: ICDE, pp. 216–225 (2007)

    Google Scholar 

  10. Mayfield, C., Neville, J., Prabhakar, S.: Eracer: a database approach for statistical inference and data cleaning. In: SIGMOD, pp. 75–86 (2010)

    Google Scholar 

  11. Wijsen, J.: Database repairing using updates. ACM Trans. Database Syst. (TODS) 30(3), 722–768 (2005)

    Article  Google Scholar 

  12. Yakout, M., Berti-Équille, L., Elmagarmid, A.K.: Don’t be scared: use scalable automatic repairing with maximal likelihood and bounded changes. In: SIGMOD, pp. 553–564 (2013)

    Google Scholar 

  13. Yakout, M., Elmagarmid, A.K., Neville, J., Ouzzani, M., Ilyas, I.F.: Guided data repair. PVLDB 4(5), 279–289 (2011)

    Google Scholar 

  14. Zheng, B., Yuan, N.J., Zheng, K., Xie, X., Sadiq, S., Zhou, X.: Approximate keyword search in semantic trajectory database. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 975–986. IEEE (2015)

    Google Scholar 

  15. Zhu, X., Wu, X.: Class noise vs. attribute noise: a quantitative study. Artif. Intell. Rev. 22(3), 177–210 (2004)

    Article  MathSciNet  MATH  Google Scholar 

Download references


This research is partially supported by Natural Science Foundation of China (Grant No. 61303019, 61402313, 61472263, 61572336), Postdoctoral scientific research funding of Jiangsu Province (No. 1501090B) National 58 batch of postdoctoral funding (No. 2015M581859) and Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Zhixu Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhou, J. et al. (2016). CrowdAidRepair: A Crowd-Aided Interactive Data Repairing Method. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, X., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9642. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32024-3

  • Online ISBN: 978-3-319-32025-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics