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What and Who
Title:Efficient Querying and Learning in Probabilistic and Temporal Databases
Speaker:Maximilian Dylla
coming from:Max-Planck-Institut für Informatik - D5
Speakers Bio:
Event Type:Promotionskolloquium
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Friday, 9 May 2014
Duration:60 Minutes
Building:E1 4
Probabilistic databases store, query, and manage large amounts of uncertain information. This thesis advances the state-of-the-art in probabilistic databases in three different ways:

1. We present a closed and complete data model for temporal probabilistic databases and analyze its complexity. Queries are posed via temporal deduction rules which induce lineage formulas capturing both time and uncertainty.

2. We devise a methodology for computing the top-k most probable query answers. It is based on first-order lineage formulas representing sets of answer candidates. Theoretically derived probability bounds on these formulas enable pruning low-probability answers.

3. We introduce the problem of learning tuple probabilities which allows updating and cleaning of probabilistic databases. We study its complexity, characterize its solutions, cast it into an optimization problem, and devise an approximation algorithm based on stochastic gradient descent.

All of the above contributions support consistency constraints and are evaluated experimentally.

Name(s):Petra Schaaf
EMail:--email address not disclosed on the web
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Attachments, File(s):
  • Petra Schaaf, 04/30/2014 01:40 PM
  • Petra Schaaf, 04/30/2014 11:00 AM -- Created document.