Workload-Aware Materialization of Junction Trees

Martino Ciaperoni, Cigdem Aslay, Aristides Gionis, Michael Mathioudakis

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Bayesian networks are popular probabilistic models that capture the conditional dependencies among a set of variables. Inference in Bayesian networks is a fundamental task for answering probabilistic queries over a subset of variables in the data. However, exact inference in Bayesian networks is NP-hard, which has prompted the development of many practical inference methods. In this paper, we focus on improving the performance of the junction-tree algorithm, a well-known method for exact inference in Bayesian networks. In particular, we seek to leverage information in the workload of probabilistic queries to obtain an optimal workload-aware materialization of junction trees, with the aim to accelerate the processing of inference queries. We devise an optimal pseudo-polynomial algorithm to tackle this problem and discuss approximation schemes. Compared to state-of-the-art approaches for efficient processing of inference queries via junction trees, our methods are the first to exploit the information provided in query workloads. Our experimentation on several real-world Bayesian networks confirms the effectiveness of our techniques in speeding-up query processing.
Original languageEnglish
Title of host publicationEDBT: 25th International Conference on Extending Database Technology : EDBT 2022
Number of pages13
Publication date2022
ISBN (Electronic)978-3-89318-086-8
Publication statusPublished - 2022
MoE publication typeA4 Article in conference proceedings
EventInternational Conference on Extending Database Technology - Edinburgh, United Kingdom
Duration: 29 Mar 20221 Apr 2022
Conference number: 25

Publication series

NameAdvances in Database Technology
ISSN (Electronic)2367-2005

Fields of Science

  • 113 Computer and information sciences

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