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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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DTSTART:20121028T030000
TZOFFSETFROM:+0200
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RDATE:20131027T030000
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DTSTART:20130331T020000
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UID:calendar.6618.field_data.0@www.diag.uniroma1.it
DTSTAMP:20260415T191624Z
CREATED:20130429T145642Z
DESCRIPTION:Abstract: The tasks of extracting Frequent Itemsets and Associa
 tion Rules are fundamental primitives in data mining and database applicat
 ions. Exact algorithms for these problems exist but require scanning the e
 ntire dataset\, possibly multiple times. When data is too big to be analyz
 ed in its entirety\, and obvious approach is to analyze a sample of the da
 ta. The major difficulty in this approach is bounding the probability of u
 nder- or over-sampling any one of an unknown number of frequent itemsets. 
 Our work circumvents this issue by applying the statistical concept of VC 
 - dimension to develop a novel technique for providing tight bounds on the
  sample size that guarantees approximation within user-specified parameter
 s (joint work with M. Riondato)\nIn a subsequent work we extend this techn
 ique to a novel randomized parallel algorithm to the problem. Our algorith
 m achieves near-linear speedup while avoiding costly replication of data..
  We formulated and implemented the algorithm in the MapReduce parallel com
 putation framework (joint work with M. Riondato\, J. DeBrabant and R. Fons
 eca).\n\nBio: Eli Upfal a professor of computer science at Brown Universit
 y\, during 2002-2007 he was also the department chair. Before coming to Br
 own in 1998 he was a researcher and project manager at the IBM Almaden Res
 earch Center in California\, and a professor at the Weizmann Institute in 
 Israel. He received an undergraduate degree in mathematics and statistics 
 and a doctorate degree in computer science from the Hebrew University in J
 erusalem\, Israel. 

His research focuses on the design and analysis of al
 gorithms. In particular\, randomized algorithms and probabilistic analysis
  of algorithms. Applications range from combinatorial and stochastic optim
 ization to routing and communication networks\, computational biology\, an
 d computational finance and statistical machine learning.
DTSTART;TZID=Europe/Paris:20130513T103000
DTEND;TZID=Europe/Paris:20130513T103000
LAST-MODIFIED:20130512T131323Z
LOCATION:DIAG\, Via Ariosto 25\, Aula B2
SUMMARY:Statistical Learning Theory Meets Big Data: Randomized Algorithms f
 or Extracting Frequent Itemsets and Association Rules - Eli Upfal\, Brown 
 University
URL;TYPE=URI:https://www.diag.uniroma1.it/node/6618
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