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T1  - Workload-aware materialization for efficient variable elimination on Bayesian networks 
SN  -  /  
UR  - http://hdl.handle.net/10138/335891 
T3  - IEEE International Conference on Data Engineering 
A1  - Aslay, Cigdem; Ciaperoni, Martino; Gionis, Aristides; Mathioudakis, Michael 
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PB  -  
Y1  - 2021 
LA  - eng 
AB  - Bayesian networks are general, well-studied probabilistic models that capture dependencies among a set of variables. Variable Elimination is a fundamental algorithm for probabilistic inference over Bayesian networks. In this paper, we propose a novel materialization method, which can lead to significant efficiency gains when processing inference queries using the Variable Elimination algorithm. In particular, we address the problem of choosing a set of intermediate results to precompute and mate... 
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KW  - 113 Computer and information sciences; probabilistic inference; materialization 
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