Keynote Speakers

 

"Erasure-Coded Distributed Cloud Storage:  Myths and Realities"

By Dr. Yih-Farn Robin Chen, Researcher, AT&T Labs Research, USA

 Bio

Yih-Farn Robin Chen is a Lead Member of the Technical Staff at AT&T Labs - Research.
His current research interests include cloud computing, mobile computing, distributed systems, World Wide Web, and IPTV. He holds a Ph.D. in Computer Science from University of California at Berkeley, an M.S. in Computer Science from University of Wisconsin, Madison, and a B.S. in Electrical Engineering from National Taiwan University. Robin is an ACM Distinguished Scientist and a Vice Chair of the International World Wide Web Conferences Steering Committee (IW3C2). He also serves on the editorial board if IEEE Internet Computing.

Abstract

Erasure codes are increasingly being used in distributed cloud storage solutions to achieve high degrees of reliability with lower storage overhead. Unfortunately, there are several myths around the technology, which is not well understood by both potential users and storage service providers. This talk attempts to debunk the myths around erasure-coded distributed cloud storage and presents performance benchmarks obtained through several open-source storage solutions deployed in geographically-distributed data centers.

This keynote speech is based on joint work with with Scott Daniels, Marios Hadjieleftheriou, Pingkai Liu, Chao Tian, and Vinay Vaishampayan.

 

 

"The Predictive Power of Search Trends and Social Media"

By Professor Takis Metaxas, Wellesley College, USA

 

The keynote is based on joint work with Eni Mustafaraj, Wellesley College, USA, and Dani Gayo-Avello, University of Oviedo, Spain.

 Bio

Panagiotis "Takis" Metaxas is a Professor of Computer Science and founder of the  Media Arts and Sciences Program at Wellesley College, and an Affiliate of the Center for Research on Computation and Society (CRCS) of Harvard University.
He holds a PhD in Computer Science from Dartmouth College and is a member of the Computing Research Association's Board of Directors, of the Advisory Board of XRDS. He is also a Guest Editor for a special issue of the Internet Research Journal on "The Power of Prediction with Social Media" and of the Special Issue of the Journal German AI Journal on Social Media.

Abstract

According to the Pew Foundation, social-networking sites, such as Twitter,  Facebook, and YouTube, are currently being used by most people connected online.  Search engines, along with e-mail, are used by practically all users online. The flow of users' opinions  expressed in social media and their interests when searching the Web has led to greater insights about what and how people think. It is also helping  segments of the world population to be informed, organize and react at  speeds not seen before.
 
Social networking sites have been credited with a number of  achievements, such as aiding revolutions, allocating resources during  disasters, and detecting flu epidemics as soon as they appear. They have also been credited with the ability to predict future events such as  movie box-office revenues, product sales, stock market fluctuations and  even election results.
 
A common explanation for these successes refers to the magic of "Big  Data" - given enough data, the claim goes, almost anything can be  predicted, much can be accomplished. However, some researchers warn that assigning complete trust and infallibility characteristics to data, no  matter how big, could lead to errors and lack of true understanding of  the reasons that produce them. We will examine how social processes  influence the digital footprints we leave on search engines and the Social Web, and will discuss some principles to maintain as we collect  and analyze them.
 
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