Projects per year
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.
|Title of host publication||Proceedings 25th International Conference on Extending Database Technology (EDBT 2022)|
|Number of pages||13|
|Publication status||Published - 2022|
|MoE publication type||A4 Article in a conference publication|
|Event||International Conference on Extending Database Technology - Edinburgh, United Kingdom|
Duration: 29 Mar 2022 → 1 Apr 2022
Conference number: 25
|Name||Advances in Database Technology|
|Conference||International Conference on Extending Database Technology|
|Period||29/03/2022 → 01/04/2022|
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- 3 Active
01/01/2020 → 31/12/2024
Project: EU: Framework programmes funding