Asking the right questions in crowd data sourcing

Rubi Boim, Ohad Greenshpan, Tova Milo, Slava Novgorodov, Neoklis Polyzotis, Wang Chiew Tan

Research output: Contribution to journalConference articlepeer-review

Abstract

Crowd-based data sourcing is a new and powerful data procurement paradigm that engages Web users to collectively contribute information. In this work, we target the problem of gathering data from the crowd in an economical and principled fashion. We present Ask It!, a system that allows interactive data sourcing applications to effectively determine which questions should be directed to which users for reducing the uncertainty about the collected data. Ask It! uses a set of novel algorithms for minimizing the number of probing (questions) required from the different users. We demonstrate the challenge and our solution in the context of a multiple-choice question game played by the ICDE'12 attendees, targeted to gather information on the conference's publications, authors and colleagues.

Original languageEnglish
Article number6228183
Pages (from-to)1261-1264
Number of pages4
JournalProceedings - International Conference on Data Engineering
DOIs
StatePublished - 2012
EventIEEE 28th International Conference on Data Engineering, ICDE 2012 - Arlington, VA, United States
Duration: 1 Apr 20125 Apr 2012

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