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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 ****