Workload-Aware Materialization of Junction Trees

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dc.contributor.author Ciaperoni, Martino
dc.contributor.author Aslay, Cigdem
dc.contributor.author Gionis, Aristides
dc.contributor.author Mathioudakis, Michael
dc.date.accessioned 2022-08-03T09:54:11Z
dc.date.available 2022-08-03T09:54:11Z
dc.date.issued 2022
dc.identifier.citation 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
dc.identifier.citation conference
dc.identifier.other PURE: 169881875
dc.identifier.other PURE UUID: 323c850c-8440-40f0-a48d-2c9df9874cdc
dc.identifier.other Scopus: 85127247652
dc.identifier.other ORCID: /0000-0003-0074-3966/work/116877563
dc.identifier.uri http://hdl.handle.net/10138/346638
dc.description.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. en
dc.format.extent 13
dc.language.iso eng
dc.publisher OpenProceedings.org
dc.relation.ispartof EDBT: 25th International Conference on Extending Database Technology
dc.relation.ispartofseries Advances in Database Technology
dc.relation.isversionof 978-3-89318-086-8
dc.rights unspecified
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.title Workload-Aware Materialization of Junction Trees en
dc.type Conference contribution
dc.contributor.organization Department of Computer Science
dc.contributor.organization Algorithmic Data Science
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.5441/002/edbt.2022.06
dc.relation.issn 2367-2005
dc.rights.accesslevel openAccess
dc.type.version publishedVersion
dc.relation.funder
dc.identifier.url https://arxiv.org/abs/2110.03475
dc.relation.grantnumber 322046

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