工作负载感知的连接树实体化
Workload-Aware Materialization of Junction Trees
摘要：
贝叶斯网络是流行的概率模型，可以捕获一组变量之间的条件依赖关系。贝叶斯网络中的推理是回答数据中变量子集的概率查询的基本任务。然而，贝叶斯网络的精确推理是\NP -困难的，这促使了许多实用的推理方法的发展。在本文中，我们专注于提高性能的连接树算法，一个众所周知的方法，在贝叶斯网络的精确推理。特别是，我们试图利用概率查询的工作负载中的信息，以获得最佳的工作负载感知的物化连接树，其目的是加快推理查询的处理。我们设计了一个最佳的伪多项式算法来解决这个问题，并讨论近似方案。与通过连接树高效处理推理查询的最先进方法相比，我们的方法是第一个利用查询工作负载中提供的信息。我们在几个真实世界的贝叶斯网络上的实验证实了我们的技术在加快查询处理的有效性。
英文摘要：
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.
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