支持不完全自治数据库查询处理的贝叶斯网络
Bayes Networks for Supporting Query Processing Over Incomplete Autonomous Databases
摘要：
随着通过自主数据源向非专业用户提供的信息不断增加，调解人对于确保有效利用现有的丰富信息变得十分重要。这些信息中介者需要处理的一个关键挑战是基础数据库中缺少属性值的不同程度的不完整性。现有的方法，如QPIAD的目的是挖掘和使用近似函数相似性（AFD）来预测和检索相关的不完整元组。这些方法对缺失值做了独立的假设-当存在包含多个相关属性的缺失值的元组时，这严重阻碍了它们的性能。在本文中，我们提出了一个原则性的概率替代方案，认为一个不完整的元组定义一个分布在完整的元组，它代表。我们在贝叶斯网络中学习这个分布。我们的方法涉及从数据库的样本中挖掘/“学习”贝叶斯网络，并使用它来进行插补（预测缺失值）和查询重写（当数据源是自治的时，检索查询约束属性不完整的相关结果）。我们目前的实证研究表明，（i）在较高水平的不完整性，当多个属性值丢失，贝叶斯网络确实提供了一个显着更高的分类准确性和（ii）使用贝叶斯网络重新制定的查询检索的相关可能的答案提供更高的精度和召回比AFD，同时保持查询处理成本可控。
英文摘要：
As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as QPIAD aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values---which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this paper, we present a principled probabilistic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. We learn this distribution in terms of Bayes networks. Our approach involves mining/"learning" Bayes networks from a sample of the database, and using it to do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). We present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
