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SUMMARY:Hung Q. Ngo (relationalAI)
DTSTART:20230426T150000Z
DTEND:20230426T161500Z
DTSTAMP:20260423T035958Z
UID:AAIT/11
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AAIT/11/">An
  Information Theoretic Approach to Estimating Query Size Bounds.</a>\nby H
 ung Q. Ngo (relationalAI) as part of Seminar on Algorithmic Aspects of Inf
 ormation Theory\n\n\nAbstract\nCardinality estimation is one of the most i
 mportant problems in database management. One aspect of cardinality estima
 tion is to derive a good upper bound on the output size of a query\, given
  a statistical profile of the inputs. In recent years\, a promising inform
 ation-theoretic approach was devised to address this problem\, leading to 
 robust cardinality estimators which are used in practice. The information 
 theoretic approach led to many interesting open questions surrounding opti
 mizing a linear function on the almost-entropic or polymatroidal cones. Th
 is talk introduces the problem\, the approach\, summarizes some known resu
 lts\, and lists open questions.\n
LOCATION:https://researchseminars.org/talk/AAIT/11/
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