Causality in Data Management: Foundations and Applications

试图理解一个事件的发生或一个动作的执行为什么以及如何影响其他事件或物体的属性，属于人类存在的本质。如果寻找因果解释不是人类最古老的活动，它肯定是其中之一，但它仍然是一个难以捉摸的概念，在许多知识领域都有研究，从哲学到计算机科学。 在大数据和数据科学的时代，分析大量数据已经成为一个越来越普遍和复杂的问题。除了从数据源中提取显式数据的传统问题之外，理解和理解数据并从这样的源中提取隐式知识已经变得至关重要。特别是，找到解释数据现象，例如，在查询答案，满足或违反语义约束，和视图内容，已被确定为一个重要的任务，解释模型，算法结果，和实际的计算实现仍然需要开发。因果关系用于解决为数据的不同表现形式提供解释的问题。 虽然因果关系已经在几个领域进行了研究，例如统计学，人工智能，经济学，但它在数据管理中的出现，特别是数据库，是最近才出现的。解决数据库中的因果关系需要对原因概念进行数学表征，调查数学模型，以及计算问题的算法和复杂性分析。在后者中，我们发现开发的高效算法计算查询答案的原因，只要有可能，或有效的近似算法时，问题的内在复杂性是高的。还必须对原因进行排序，确定最相关的原因，并为此引入了责任概念。围绕责任计算出现了几个计算问题。PI已经在这一领域取得了令人感兴趣的成果。然而，数据库中因果关系的许多方面和问题，以及更普遍的数据管理，仍然是开放的。 在这个建议中，我们解决因果关系的语义丰富的数据源的背景下，目的是提出和调查的因果关系模型的场景，如基于本体的数据访问，虚拟数据集成，多维数据库，等等。我们还讨论了更基本的问题，例如原因的逻辑描述，这应该告诉我们嵌入数据源的什么理论可以用于推断原因以及如何推断原因。我们研究从（可能是概率性的）数据源中学习因果关系的问题，通过开发专门为数据库量身定制的机器学习方法，这些数据库通常缺乏对学习有用的额外语义信息。

Trying to understand why and how the occurrence of an event or the execution of an action affect other events or properties of objects belongs to the essence of human existence. If the search for causal explanations is not the oldest activity performed by humans, it is certainly one of them, yet it still has been an elusive concept that has been studied in many fields of knowledge, ranging from philosophy to computer science. In these times of big data and data science, analyzing large volumes of data has become an increasingly common and complex problem. Beyond the traditional problem of extracting explicit data from a data source, it has become crucial to understand and make sense of the data, and extract implicit knowledge from such a source. In particular, finding explanations for data phenomena as shown, for example, in query answers, satisfaction or violation of semantic constraints, and view contents, has been identified as an important task, for which explanation models, algorithmic results, and practical computational implementations still need to be developed. Causality is used to address the problem of providing explanations for different forms of manifestations of data. Although causality has been investigated in several areas, e.g. statistics, artificial intelligence, economics, its emergence in data management, and databases in particular, is quite recent. Addressing causality in databases requires a mathematical characterization of the notion of cause, the investigation of the mathematical model, and the algorithmic and complexity analysis of computational problems. Among the latter, we find development of efficient algorithms for computing causes for query answers, whenever possible, or of efficient approximation algorithms when the intrinsic complexity of the problem is high. It also becomes necessary to rank causes, identifying those most relevant, for which the notion of responsibility has been introduced. Several computational problems emerge around responsibility computation. The PI has already obtained interesting results in this area. However, many aspects and problems of causality in databases, and more generally in data management, are still open. In this proposal we address causality in the context of semantically enriched data sources, with the aim to propose and investigate a model of causality in scenarios such as ontology-based data access, virtual data integration, and multidimensional databases, among others. We also go into more fundamental problems, such as the logical characterization of causes, which should tell us what theory embedding the data source can be used to infer causes and how. We investigate the problem of learning causal relations from (possibly probabilistic) data sources, by developing machine learning methods specifically tailored for databases, which normally lack the additional semantic information that is useful for learning.