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Approximate Lifted Inference with Probabilistic Databases
"... This paper proposes a new approach for approximate evaluation of #Phard queries with probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking th ..."
Abstract

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This paper proposes a new approach for approximate evaluation of #Phard queries with probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known results of PTIME selfjoinfree conjunctive queries: A query is safe if and only if our algorithm returns one single plan. We also apply three relational query optimization techniques to evaluate all minimal safe plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over nonprobabilistic methods for ranking query answers. 1.
Approximate Lifted Inference in Probabilistic Databases
"... This paper proposes a new approach for approximate evaluation of #Phard queries over probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking ..."
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This paper proposes a new approach for approximate evaluation of #Phard queries over probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known results of PTIME selfjoin free conjunctive queries: A query is safe if and only if our algorithm returns one single plan. We also apply three relational query optimization techniques to evaluate all minimal safe plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over nonprobabilistic methods for ranking query answers.
Authors ' Addresses
, 2014
"... We would like to thank Floris Geerts and Rainer Gemulla for helpful technical discussions. We thank Radu Curticapean for pointing out Bezout's theorem. Learning the parameters of complex probabilisticrelational models from labeled training data is a standard technique in machine learning, whi ..."
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We would like to thank Floris Geerts and Rainer Gemulla for helpful technical discussions. We thank Radu Curticapean for pointing out Bezout's theorem. Learning the parameters of complex probabilisticrelational models from labeled training data is a standard technique in machine learning, which has been intensively studied in the subeld of Statistical Relational Learning (SRL), butso farthis is still an underinvestigated topic in the context of Probabilistic Databases (PDBs). In this paper, we focus on learning the probability values of base tuples in a PDB from query answers, the latter of which are represented as labeled lineage formulas. Specically, we consider labels in the form of pairs, each consisting of a Boolean lineage formula and a marginal probability that comes attached to the corresponding query answer. The resulting learning problem can be viewed as the inverse problem to condence computations in PDBs: given a set of labeled query answers, learn the probability values of the base tuples, such that the marginal prob