Workload-Aware Materialization of Junction Trees

TL;DR

This paper introduces a workload-aware approach to optimize junction-tree materialization for Bayesian network inference, significantly improving query processing speed by leveraging query workload information.

Contribution

It presents the first method to exploit query workload data for optimal junction-tree materialization, including an exact pseudo-polynomial algorithm and approximation schemes.

Findings

Significant speed-up in inference query processing on real-world Bayesian networks.

First to utilize workload information for junction-tree optimization.

Effective approximation schemes demonstrated in experiments.

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.