为什么会这样呢？或者为什么不呢？解释查询答案的功能因果关系
Why so? or Why no? Functional Causality for Explaining Query Answers
摘要：
在本文中，我们提出因果关系作为解释查询答案和非答案的统一框架，从而概括和扩展了之前提出的几种出处和缺失查询结果解释方法。我们从Halpern和Pearl对实际原因的充分研究的定义开始发展我们的框架。在确定了一些不可取的特点，原来的定义，我们提出了一个完善的定义因果关系的功能性原因与几个可取的属性。这些属性允许我们在数据库上下文中应用因果关系的概念，并以几种方式统一地应用它来定义查询结果的原因及其各自的贡献：（i）我们可以对出处和非答案两者进行建模，（ii）我们可以将解释定义为输入关系中的数据或查询计划中的关系操作，（3）我们可以对个别原因的责任程度进行分级，从而使我们能够对原因进行排序。特别是，我们的方法使我们能够解释的贡献关系的聚合功能和排名原因根据各自的责任。我们给出了复杂性的结果，并描述了多项式算法在易处理的情况下，因果关系的评估。在整个文件中，我们用几个例子说明了我们的框架的适用性。总体而言，我们在本文中开发的因果关系理论在数据库环境中的理论基础。
英文摘要：
In this paper, we propose causality as a unified framework to explain query answers and non-answers, thus generalizing and extending several previously proposed approaches of provenance and missing query result explanations. We develop our framework starting from the well-studied definition of actual causes by Halpern and Pearl. After identifying some undesirable characteristics of the original definition, we propose functional causes as a refined definition of causality with several desirable properties. These properties allow us to apply our notion of causality in a database context and apply it uniformly to define the causes of query results and their individual contributions in several ways: (i) we can model both provenance as well as non-answers, (ii) we can define explanations as either data in the input relations or relational operations in a query plan, and (iii) we can give graded degrees of responsibility to individual causes, thus allowing us to rank causes. In particular, our approach allows us to explain contributions to relational aggregate functions and to rank causes according to their respective responsibilities. We give complexity results and describe polynomial algorithms for evaluating causality in tractable cases. Throughout the paper, we illustrate the applicability of our framework with several examples. Overall, we develop in this paper the theoretical foundations of causality theory in a database context.
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。
