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SUMMARY:Representing and Querying Large-scale Uncertainty - Prithviraj Sen
DTSTART:20110407T083000Z
DTEND:20110407T093000Z
UID:TALK30570@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:A number of real-world applications require modeling uncertain
 ty. Examples include information extraction\, information integration\, da
 ta collected from sensor networks\, social networks and scientific databas
 es just to name a few. Traditionally\, most of these applications have bee
 n tackled in isolation even though the core operations of modeling and rea
 soning under uncertainty are common to all of them. Probabilistic database
 s\, an area of research in the intersection of machine learning and large-
 scale data management\, hold the promise to provide a common framework tha
 t solves all applications requiring uncertainty management and enhance the
 ir utility by providing an easy-to-use\, declarative querying interface. I
 n this talk\, I will describe some of my recent contributions to this area
  of research touching upon efficient query evaluation\, different represen
 tation schemes in increasing order of complexity and efficient inference a
 lgorithms. Designing a common framework requires that we first gain an in-
 depth understanding of the targetted range of applications. I harbour an\n
 independent interest in a number of such applications and will also be des
 cribing my recent work in information retrieval\, information extraction a
 nd information integration.\n
LOCATION:Small lecture theatre\, Microsoft Research Ltd\, 7 J J Thomson Av
 enue (Off Madingley Road)\, Cambridge
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