Skip to main content

Conclusion

  • Chapter
  • First Online:
Crowdsourced Data Management
  • 382 Accesses

Abstract

Crowdsourcing has become more and more prevalent in data management and analytics. This book provides a comprehensive review of crowdsourced data management, including motivation, applications, techniques, and existing systems. This chapter first summarizes this book in Sect. 8.2 and then provides several research challenges and opportunities in Sect. 8.2.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Chinacrowd. http://www.chinacrowds.com

  2. Waze. https://www.waze.com

  3. Chen, Z., Fu, R., Zhao, Z., Liu, Z., Xia, L., Chen, L., Cheng, P., Cao, C.C., Tong, Y., Zhang, C.J.: gmission: a general spatial crowdsourcing platform. PVLDB 7(13), 1629–1632 (2014)

    Article  Google Scholar 

  4. Haas, D., Ansel, J., Gu, L., Marcus, A.: Argonaut: Macrotask crowdsourcing for complex data processing. PVLDB 8(12), 1642–1653 (2015)

    Google Scholar 

  5. Mozafari, B., Sarkar, P., Franklin, M., Jordan, M., Madden, S.: Scaling up crowd-sourcing to very large datasets: a case for active learning. PVLDB 8(2), 125–136 (2014)

    Google Scholar 

  6. Wang, J., Krishnan, S., Franklin, M.J., Goldberg, K., Kraska, T., Milo, T.: A sample-and-clean framework for fast and accurate query processing on dirty data. In: SIGMOD, pp. 469–480 (2014)

    Google Scholar 

  7. Wu, S., Wang, X., Wang, S., Zhang, Z., Tung, A.K.H.: K-anonymity for crowdsourcing database. TKDE 26(9), 2207–2221 (2014)

    Google Scholar 

  8. Yuan, D., Li, G., Li, Q., Zheng, Y.: Sybil defense in crowdsourcing platforms. In: CIKM, pp. 1529–1538 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Li, G., Wang, J., Zheng, Y., Fan, J., Franklin, M.J. (2018). Conclusion. In: Crowdsourced Data Management. Springer, Singapore. https://doi.org/10.1007/978-981-10-7847-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7847-7_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7846-0

  • Online ISBN: 978-981-10-7847-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics