Skip to main content
SpringerLink
Log in

Crowd Mining and Analysis

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

  • 79 Accesses

FormalPara Synonyms

Crowd data mining, Crowd mining

Definition

Crowd mining is the process of identifying data patterns in human knowledge, with the assistance of a crowd of web users. The focus is on domain areas in which data is partially or entirely undocumented and where humans are the main source of knowledge, such as data that involves people’s habits, experiences, and opinions. A key challenge in mining such data is that the human knowledge forms an open world and it is thus difficult to know what kind of information one should be looking for.

In classic databases, a similar problem is addressed by data mining techniques that identify interesting patterns in recorded data such as relational databases or textual documents. These techniques, however, are not suitable for the crowd. This is mainly due to properties of the human memory, such as the tendency to remember simple trends and summaries rather than exact details, which should be taken into consideration when gathering and...

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

Access this chapter

Log in via an institution

Institutional subscriptions

Recommended Reading

  1. Doan A, Franklin M, Kossmann D, Kraska T. Crowdsourcing applications and platforms: a data management perspective. PVLDB. 2011;4(12):1508–9.

    Google Scholar 

  2. Davidson SB, Khanna S, Milo T, Roy S. Using the crowd for top-k and group-by queries. In: ICDT; 2013. p. 225–36.

    Google Scholar 

  3. Franklin MJ, Kossmann D, Kraska T, Ramesh S, Xin R. CrowdDB: answering queries with crowdsourcing. In: SIGMOD; 2011. p. 61–72.

    Google Scholar 

  4. Marcus A, Wu E, Karger DR, Madden S, Miller RC. Human-powered Sorts and Joins. PVLDB. 2011;5(1):13–24.

    Google Scholar 

  5. Parameswaran AG, Park H, Garcia-Molina H, Polyzotis N, Widom J. Deco: declarative crowdsourcing. In: CIKM; 2012. p. 1203–12.

    Google Scholar 

  6. Trushkowsky B, Kraska T, Franklin MJ, Sarkar P. Crowdsourced enumeration queries. In: ICDE; 2013. p. 673–84.

    Google Scholar 

  7. Venetis P, Garcia-Molina H, Huang K, Polyzotis N. Max algorithms in crowdsourcing environments. In: WWW; 2012. p. 989–98.

    Google Scholar 

  8. Amsterdamer Y, Grossman Y, Milo T, Senellart P. Crowd mining. In: SIGMOD; 2013. p. 241–52.

    Google Scholar 

  9. Amarilli A, Amsterdamer Y, Milo T. On the complexity of mining itemsets from the crowd using taxonomies. In: ICDT; 2014. p. 15–25.

    Google Scholar 

  10. Amsterdamer Y, Davidson SB, Milo T, Novgorodov S, Somech A. OASSIS: query driven crowd mining yael. In: SIGMOD; 2014. p. 1–12.

    Google Scholar 

  11. Bradburn NM, Rips LJ, Shevell SK. Answering autobiographical questions: the impact of memory and inference on surveys. Science. 1987;236(4798): 158–61.

    Article  Google Scholar 

  12. Srikant R, Agrawal R. Mining generalized association rules. In: VLDB; 1995. p. 407–19.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Bar Ilan University, Ramat Gan, Israel

    Yael Amsterdamer

  2. School of Computer Science, Tel Aviv University, Tel Aviv, Israel

    Tova Milo

Authors
  1. Yael Amsterdamer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tova Milo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yael Amsterdamer .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China

    Reynold Cheng PhD, Associate Professor

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this entry

Cite this entry

Amsterdamer, Y., Milo, T. (2017). Crowd Mining and Analysis. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_80657-2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_80657-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4899-7993-3

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation