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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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DTSTART:20161030T030000
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DTSTART:20160327T020000
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UID:calendar.7385.field_data.0@diag.uniroma1.it
DTSTAMP:20260317T034652Z
CREATED:20161017T122526Z
DESCRIPTION:Nowadays stream analysis is used in many context where the amou
 nt of data and/or the rate at which it is generated rules out other approa
 ches (e.g.\, batch processing). The data streaming model provides randomiz
 ed and/or approximated solutions to compute specific functions over (distr
 ibuted) stream(s) of data-items in worst case scenarios\, while striving f
 or small resources usage. In particular\, we look into two classical and r
 elated data streaming problems: frequency estimation and (distributed) hea
 vy hitters. Solutions to these problems have a wide area of application\, 
 spanning from data bases to network monitoring. A less common field of app
 lication is stream processing which is somehow complementary and more prac
 tical\, providing efficient and highly scalable frameworks to perform soft
  real-time generic computation on streams\, relying on cloud computing. Th
 is duality allows us to apply data streaming solutions to optimize stream 
 processing systems. In this talk\, we introduce a novel algorithm to track
  heavy hitters in distributed streams and two extensions of a well-known a
 lgorithm to estimate the frequencies of data items. We also tackle two rel
 ated problems and their solution: provide even partitioning of the item un
 iverse based on their weights and provide an estimation of the values carr
 ied by the items of the stream. We then apply these results to both networ
 k monitoring and stream processing. In particular\, we leverage these solu
 tions to perform load shedding as well as to load balance parallelized ope
 rators in stream processing systems.Bio: Nicolò Rivetti di Val Cervo recen
 tly got a PhD in co-turoship between the LINA / University of Nantes (Fran
 ce) and the DIAG / Sapienza University of Rome. He got both his B.S. and M
 .S. in Engineering in Computer Science at DIAG / Sapienza University of Ro
 me. Nicolò's research interests are currently focussed on the Data Streami
 ng model where and in particular on the design of algorithms for the estim
 ation of functions over massively distributed data streams. His interests 
 also span over other fields dealing with big data\, including Network Moni
 toring and Stream Processing.
DTSTART;TZID=Europe/Paris:20161026T120000
DTEND;TZID=Europe/Paris:20161026T120000
LAST-MODIFIED:20230710T173816Z
LOCATION:DIAG B203
SUMMARY:Efficient Stream Analysis and its Application to Big Data Processing
  - Nicolò Rivetti di Val Cervo
URL;TYPE=URI:https://diag.uniroma1.it/node/7385
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