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Data-driven Crowdsourcing via Online Social Users
PhD Thesis Proposal Defence
Title: "Data-driven Crowdsourcing via Online Social Users"
by
Mr. Chen CAO
Abstract:
Human computation is a long-existing concept and has been practiced for
centuries. Specifically, whenever a “human” serves to “compute”, human
computation is observed. This leads to a history of Human Computation even
longer than that of electronic computer. Now with the development of Internet
web service, the workforce of human computation is broadened to a vast pool of
crowds, instead of designated exerts or employees. This type of outsourcing to
crowds, a.k.a. crowdsourcing, ushers in the new computation paradigm of
Crowdsourced Human Computation.
In this proposal, we demonstrate the power of Crowdsourced Human Computation on
data-driven applications. We introduce the application on data filtering,
ranking, joining and other operators. Moreover, we show that most of these
applications can be decomposed into binary decision making tasks for human
workforce. Then we illustrate the majority voting over the decision making as
crowdsourced answer aggregator and discuss its properties. There are three
major challenges to establish high-performance crowdsourcing applications;
therefore we present corresponding techniques as follows:
Quality: Jury-selection problem to solve "Whom to Ask" challenge to improve
answer quality under majority voting;
Cost: WiseMarket as a new crowdsourcing paradigm to conduct payment with less
cost and higher quality;
Latency: Model of tasks completion time and tuning strategies to accelerate
task completion.
In the end, we show future research direction and propose the targeted work to
finalize the theoretical and technical foundation.
Date: Wednesday, 7 May 2014
Time: 4:00pm - 6:00pm
Venue: Room 3501
lifts 25/26
Committee Members: Dr. Lei Chen (Supervisor)
Dr. Raymond Wong (Chairperson)
Dr. Pan Hui
Dr. Qiong Luo
**** ALL are Welcome ****