A PROBABILISTIC FRAMEWORK ON MACHINE-CROWD COLLABORATION AND ITS APPLICATIONS ON DATA INTEGRATION

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis 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 humanintrinsic 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 a specific human-machine hybrid system;
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:			Friday, 7 August 2015

Time:			4:00pm - 6:00pm

Venue:			Room 2131B
 			Lift 19

Chairman:		Prof. Wai-Ho Mow (ECE)

Committee Members:	Prof. Lei Chen (Supervisor)
 			Prof. Pan Hui
 			Prof. Qian Zhang
 			Prof. Xianhua Peng (MATH)
 			Prof. Guoliang Li (Comp. Sci., Tsinghua Univ.)


**** ALL are Welcome ****