Ciaperoni , M , Aslay , C , Gionis , A & Mathioudakis , M 2022 , Workload-Aware Materialization of Junction Trees . in EDBT: 25th International Conference on Extending Database Technology : EDBT 2022 . Advances in Database Technology , vol. 25 , OpenProceedings.org , pp. 65-77 , International Conference on Extending Database Technology , Edinburgh , United Kingdom , 29/03/2022 . https://doi.org/10.5441/002/edbt.2022.06
Title: | Workload-Aware Materialization of Junction Trees |
Author: | Ciaperoni, Martino; Aslay, Cigdem; Gionis, Aristides; Mathioudakis, Michael |
Contributor organization: | Department of Computer Science Algorithmic Data Science |
Publisher: | OpenProceedings.org |
Date: | 2022 |
Language: | eng |
Number of pages: | 13 |
Belongs to series: | EDBT: 25th International Conference on Extending Database Technology |
Belongs to series: | Advances in Database Technology |
ISBN: | 978-3-89318-086-8 |
ISSN: | 2367-2005 |
DOI: | https://doi.org/10.5441/002/edbt.2022.06 |
URI: | http://hdl.handle.net/10138/346638 |
Abstract: | 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. |
Subject: | 113 Computer and information sciences |
Peer reviewed: | Yes |
Rights: | unspecified |
Usage restriction: | openAccess |
Self-archived version: | publishedVersion |
Funder: | |
Grant number: | 322046 |
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