<html><head><style type='text/css'>p { margin: 0; }</style></head><body><div style='font-family: arial,helvetica,sans-serif; font-size: 12pt; color: #000000'><br><div style="font-family: arial,helvetica,sans-serif; font-size: 12pt; color: #000000"><div style="color:#000;font-weight:normal;font-style:normal;text-decoration:none;font-family:Helvetica,Arial,sans-serif;font-size:12pt;"><div style="font-family: arial,helvetica,sans-serif; font-size: 12pt; color: #000000"><div style="color:#000;font-weight:normal;font-style:normal;text-decoration:none;font-family:Helvetica,Arial,sans-serif;font-size:12pt;"><b>Human-Powered Data Management</b>
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<b><a href="http://www-cs-students.stanford.edu/%7Eadityagp/" target="_blank">Aditya Parameswaran</a></b>, <a href="http://www.stanford.edu/" target="_blank">Stanford University</a>
<br>Monday, March 11, 2013, 4:30pm<br>Computer Science 105<br>
<br>
Fully automated algorithms are inadequate for a number of data analysis
tasks, especially those involving images, video, or text. Thus, there is
a need to combine “human computation” (or crowdsourcing), together with
traditional computation, in order to improve the process of
understanding and analyzing data. My thesis addresses several topics in
the general area of human-powered data management. I design algorithms
and systems for combining human and traditional computation for: (a)
data processing, e.g., using humans to help sort, cluster, or clean
data; (b) data extraction, e.g., having humans help create structured
data from information in unstructured web pages; and (c) data gathering,
i.e., asking humans to provide data that they know about or can locate,
but that would be difficult to gather automatically. My focus in all of
these areas is to find solutions that expend as few resources as
possible (e.g., time waiting, human effort, or money spent), while still
providing high quality results.<br><br><p>
In this talk, I will first present a broad perspective of our research
on human-powered data management, and I will describe some systems and
applications that have motivated our research. I will then present
details of one of the problems we have addressed: filtering large data
sets with the aid of humans. Finally I will argue that human-powered
data management is an area in its infancy, by describing a number of
open problems I intend to address in my future research program. <br></p><p><br></p>
Aditya Parameswaran is a Ph.D. student in the InfoLab at Stanford
University, advised by Prof. Hector Garcia-Molina. He is broadly
interested in data management, with research results in human
computation, information extraction, and recommendation systems. Aditya
is a recipient of the Key Scientific Challenges Award from Yahoo!
Research (2010), two best-of-conference citations (VLDB 2010 and KDD
2012), and the Terry Groswith graduate fellowship at Stanford
University.
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