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SUMMARY:Querying Big\, Dynamic\, Distributed Data
DTSTART:20130620T151500
DTEND:20130620T161500
DTSTAMP:20260407T095307Z
UID:4555f228b42d35170650790ba1f316f47ab2ee9a536cedcc7e78c32f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Minos Garofalakis\, Technical University of Crete\nEffe
 ctive Big Data management and analysis poses several difficult challenges 
 for modern database architectures. One key such challenge arises from the 
 naturally streaming nature of big data\, which mandates efficient algorith
 ms for querying and analyzing massive\, continuous data streams (that is\,
  data that is seen only once and in a fixed order) with limited memory and
  CPU-time resources. Such streams arise naturally in emerging large-scale 
 event monitoring applications\; for instance\, network-operations monitori
 ng in large ISPs\, where usage information from numerous sites needs to be
  continuously collected and analyzed for interesting trends. In addition t
 o memory- and time-efficiency concerns\, the inherently distributed nature
  of such applications also raises important communication-efficiency issue
 s\, making it critical to carefully optimize the use of the underlying net
 work infrastructure. In this talk\, we introduce the distributed data stre
 aming model\, and discuss some of our recent results on tracking complex q
 ueries over massive distributed streams\, as well as new research directio
 ns in this space.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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