Projects per year
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.
Original language | English |
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Title of host publication | EDBT: 25th International Conference on Extending Database Technology : EDBT 2022 |
Number of pages | 13 |
Publisher | OpenProceedings.org |
Publication date | 2022 |
Pages | 65-77 |
ISBN (Electronic) | 978-3-89318-086-8 |
DOIs | |
Publication status | Published - 2022 |
MoE publication type | A4 Article in conference proceedings |
Event | International Conference on Extending Database Technology - Edinburgh, United Kingdom Duration: 29 Mar 2022 → 1 Apr 2022 Conference number: 25 |
Publication series
Name | Advances in Database Technology |
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Volume | 25 |
ISSN (Electronic) | 2367-2005 |
Fields of Science
- 113 Computer and information sciences
Projects
- 1 Finished
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MLDB: Model Management Systems: Machine learning meets Database Systems
Mathioudakis, M. (Project manager), Gionis, A. (Co-Principal Investigator), Mahadevan, A. (Participant), Maniatis, A. (Participant), Merchant, A. (Participant) & Pai, S. G. (Participant)
Suomen Akatemia Projektilaskutus
01/09/2019 → 31/12/2023
Project: Research Council of Finland: Academy Project