Querying Big, Dynamic, Distributed Data
Speaker:

Prof. Minos Garofalakis

Date: 18/07/2013
University: Department of Electronic & Computer Engineering
Room : ΑΘΗΝΑ - Ερευνητικό Κέντρο Καινοτομίας στις Τεχνολογίες της Πληροφορίας, των Επικοινωνιών και της Γνώσης
Time: 16:00
Slides:
Abstract:


Effective 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 algorithms 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 monitoring in large ISPs, where usage information 
from numerous sites needs to be continuously collected and analyzed for 
interesting trends. In addition to memory- and time-efficiency concerns, 
the inherently distributed nature of such applications also raises 
important communication-efficiency issues, making it critical to 
carefully optimize the use of the underlying network infrastructure. In 
this talk, we introduce the distributed data streaming model, and 
discuss some of our recent results on tracking complex queries over 
massive distributed streams, as well as new research directions in this 
space.
 

 

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