A Probabilistic Framework on Machine-Crowd Collaboration and its Applications on Data Integration

PhD Thesis Proposal Defence


Title: "A Probabilistic Framework on Machine-Crowd Collaboration and its
Applications on Data Integration"

by

Mr. Chen ZHANG


Abstract:

Recently, the popularity of crowdsourcing has brought a new opportunity 
for engaging human intelligence into the process of data analysis. 
Existing works on crowdsourcing have developed sophisticated methods by 
utilizing the crowd as a new kind of processor, a.k.a HPU. One of the 
drawbacks of these works is that they treat the crowd as the sole 
information source for the human-intrinsic queries. However, on many 
applications, such human-intrinsic queries can be also answered by 
machine-alone systems (i.e. CPUs). On the one hand, the latency of using 
HPUs to answer queries is much longer than that of CPUs, and the monetary 
cost of HPUs is often high (e.g. crowdsoucing on Amazon Mechanical Turk), 
but on the other hand, the answers obtained from CPUs often have high 
uncertainty due to its incapability to recognize human-intrinsic 
semantics. Therefore, it is natural to ask why we cannot combine the power 
of CPUs and the wisdom of HPUs to answer human-intrinsic queries 
accurately and fast, which is exactly the motivation of this work.

To summarize, our study covers four following aspects:

1) We propose three new specific human-machine hybrid system in three 
different application background, to improve the data quality

2) We design a novel crowd-machine hybrid system of uncertain data 
cleaning ;

3) We study the classic problem of schema mapping in the new crowdsourcing 
perspective;

We validate our solutions through extensive experiments and discuss 
several interesting research directions of CPU and HPU hybrid systems on 
data integration.


Date:			Monday, 4 May 2015

Time:                  	5:30pm - 7:30pm

Venue:                  Room 3501
                         lifts 25/26

Committee Members:	Dr. Lei Chen (Supervisor)
  			Dr. Pan Hui (Chairperson)
 			Dr. Raymond Wong
  			Dr. Ke Yi


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