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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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DTSTART:20181028T030000
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TZOFFSETTO:+0100
RDATE:20191027T030000
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BEGIN:DAYLIGHT
DTSTART:20190331T020000
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UID:calendar.18160.field_data.0@www.u-gov-ricerca.uniroma1.it
DTSTAMP:20260404T191311Z
CREATED:20190411T084033Z
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 ight: 17.0px\; font: 15.2px Arial\; color: #52514b\; -webkit-text-stroke: 
 #52514b\; background-color: #ffffff}\nspan.s1 {font-kerning: none}\nApril 
 15th 2019\, 10.00 - 14.00\, Room B203\, DIAG\, Via Ariosto 25. April 16th 
 2019\, 9.00 - 13.00\, Room B203\, DIAG\, Via Ariosto 25. As a fundamental 
 tool in modeling and analyzing real world data\, large-scale algorithms ar
 e a central part of any tool set for big data analysis. Processing dataset
 s with hundreds of billions of entries is only possible via developing dis
 tributed algorithms under distributed frameworks such as MapReduce\, Prege
 l\, Gigraph\, and alike. For these distributed algorithms to work well in 
 practice\, we need to take into account several metrics such as the number
  of rounds of computation and the communication complexity of each round. 
 For example\, given the popularity and ease-of-use of MapReduce framework\
 , developing practical algorithms with good theoretical guarantees for bas
 ic algorithmic primitives is a problem of great importance. In this course
 \, we discuss how to design and implement algorithms based on traditional 
 MapReduce architecture. In this regard\, we discuss various basic algorith
 mic problems such as computing connected components\, maximum matching\, M
 ST\, counting triangle\, clustering\, diversity maximization and so on so 
 for. In particular\, we discuss a computation model for MapReduce and desc
 ribe the sampling&filtering\, and core-set techniques to develop efficient
  algorithms in this framework.     
DTSTART;TZID=Europe/Paris:20190415T100000
DTEND;TZID=Europe/Paris:20190416T130000
LAST-MODIFIED:20200723T170301Z
LOCATION:DIAG - Via Ariosto 25\, Room B203
SUMMARY:Distributed Models\, Mapreduce and Large Scale Algorithms - Silvio 
 Lattanzi (Google Research)
URL;TYPE=URI:http://www.u-gov-ricerca.uniroma1.it/node/18160
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