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CrowdIQ: A Declarative Crowdsourcing Platform for Improving the Quality of Web Tables

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10367))

Abstract

Web tables provide us with high-quality sources of structured data. However, we could not use those valuable tables directly owing to various problems such as conflict data and missing headers. We present CrowdIQ, a scalable platform that integrates crowdsourcing technology for improving the quality of web tables. We design CrowdIQL, which is a declarative language aiming at helping requesters operate tables more exactly and flexibly. Crowdsourcing task is also optimized in this platform by providing candidate items and minimizing useless data, which help requesters to get higher quality tables with less cost.

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References

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Acknowledgment

This work is supported by National Natural Science Foundation of China (Grant No. 61370060). We would also like to give our thanks for the support from Microsoft Research Asia.

Author information

Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

    Yihai Xi, Ning Wang, Xiaoyu Wu, Yuqing Bao & Wutong Zhou

Authors
  1. Yihai Xi
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  2. Ning Wang
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  3. Xiaoyu Wu
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  4. Yuqing Bao
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  5. Wutong Zhou
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Corresponding author

Correspondence to Ning Wang .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University , Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

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Cite this paper

Xi, Y., Wang, N., Wu, X., Bao, Y., Zhou, W. (2017). CrowdIQ: A Declarative Crowdsourcing Platform for Improving the Quality of Web Tables. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_28

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  • DOI: https://doi.org/10.1007/978-3-319-63564-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63563-7

  • Online ISBN: 978-3-319-63564-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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