Many popular sites, such as Wikipedia and Tripadvisor, rely on public participation to gather information - a process known as crowd data sourcing. While this kind of collective intelligence is extremely valuable, it is also fallible, and policing such sites for inaccuracies or missing material is a costly undertaking. In this talk we will overview the MoDaS project that investigates how database technology can be put to work to effectively gather information from the public, efficiently moderate the process, and identify questionable input with minimal human interaction [1-4, 7]. We will consider the logical, algorithmic, and methodological foundations for the management of large scale crowd-sourced data as well as the development of applications over such information.


Query Processing Collective Intelligence Database Technology Planning Query Schema Expansion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Amsterdamer, Y., Grossman, Y., Milo, T., Senellart, P.: Crowd mining. In: SIGMOD (2013)Google Scholar
  2. 2.
    Boim, R., Greenshpan, O., Milo, T., Novgorodov, S., Polyzotis, N., Tan, W.-C.: Asking the right questions in crowd data sourcing. In: ICDE, pp. 1261–1264 (2012)Google Scholar
  3. 3.
    Davidson, S., Khanna, S., Milo, T., Roy, S.: Using the crowd for top-k and group-by queries. In: ICDT (2013)Google Scholar
  4. 4.
    Deutch, D., Greenshpan, O., Kostenko, B., Milo, T.: Declarative platform for data sourcing games. In: WWW, pp. 779–788 (2012)Google Scholar
  5. 5.
    Franklin, M.J., Kossmann, D., Kraska, T., Ramesh, S., Xin, R.: Crowddb: answering queries with crowdsourcing. In: SIGMOD (2011)Google Scholar
  6. 6.
    Guo, S., Parameswaran, A.G., Garcia-Molina, H.: So who won?: dynamic max discovery with the crowd. In: SIGMOD Conference, pp. 385–396 (2012)Google Scholar
  7. 7.
    Kaplan, H., Lotosh, I., Milo, T., Novgorodov, S.: Answering planning queries with the crowd. In: VLDB (2013)Google Scholar
  8. 8.
    Liu, X., Lu, M., Ooi, B.C., Shen, Y., Wu, S., Zhang, M.: Cdas: A crowdsourcing data analytics system. PVLDB 5(10), 1040–1051 (2012)Google Scholar
  9. 9.
    Marcus, A., Wu, E., Madden, S., Miller, R.C.: Crowdsourced databases: Query processing with people. In: CIDR, pp. 211–214 (2011)Google Scholar
  10. 10.
    Park, H., Pang, R., Parameswaran, A.G., Garcia-Molina, H., Polyzotis, N., Widom, J.: Deco: A system for declarative crowdsourcing. PVLDB 5(12), 1990–1993 (2012)Google Scholar
  11. 11.
    Selke, J., Lofi, C., Balke, W.-T.: Pushing the boundaries of crowd-enabled databases with query-driven schema expansion. PVLDB 5(6), 538–549 (2012)Google Scholar
  12. 12.
    Wang, J., Kraska, T., Franklin, M.J., Feng, J.: Crowder: Crowdsourcing entity resolution. PVLDB 5(11), 1483–1494 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tova Milo
    • 1
  1. 1.School of Computer ScienceTel Aviv UniversityIsrael

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