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Journal Articles Intelligent Data Analysis Year : 2016

Probabilistic relational model benchmark generation: Principle and application


The validation of any database mining methodology goes through an evaluation process where benchmarks availability is essential. In this paper, we aim to randomly generate relational database benchmarks that allow to check probabilistic dependencies among the attributes. We are particularly interested in Probabilistic relational models (PRMs). These latter extend Bayesian networks (BNs) to a relational data mining context that enable effective and robust reasoning about relational data structures. Even though a panoply of works have focused, separately, on Bayesian networks and relational databases random generation, no work has been identified for PRMs on that track. This paper provides an algorithmic approach allowing to generate random PRMs from scratch to cover the absence of generation process. The proposed method allows to generate PRMs as well as synthetic relational data from a randomly generated relational schema and a random set of probabilistic dependencies. This can be of interest for machine learning researchers to evaluate their proposals in a common framework, as for databases designers to evaluate the effectiveness of the components of a database management system.
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hal-01150688 , version 1 (15-04-2020)



Mouna Ben Ishak, Philippe Leray, Nahla Ben Amor. Probabilistic relational model benchmark generation: Principle and application. Intelligent Data Analysis, 2016, 20 (3), pp.615-635. ⟨10.3233/IDA-160823⟩. ⟨hal-01150688⟩
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