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对象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，它当然是其中之一，但它仍然是一个难以捉摸的概念，已经在从哲学到计算机科学的许多知识领域进行了研究。*在这个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常见和复杂的问题。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含的知识变得至关重要。特别是，为例如在查询答案、满足或违反语义约束和查看内容中所示的数据现象找到解释已经被确定为一项重要的任务，对于该任务，仍然需要开发解释模型、算法结果和实际计算实现。因果关系被用来解决为数据的不同表现形式提供解释的问题。*虽然在统计学、人工智能、经济学等几个领域对因果关系进行了研究，但它在数据管理，特别是数据库中的出现是相当近的事情。解决数据库中的因果关系需要对原因概念进行数学描述，对数学模型进行调查，并对计算问题进行算法和复杂性分析。在后者中，我们发现在可能的情况下用于计算查询答案的原因的高效算法的发展，或者当问题的内在复杂性很高时的高效近似算法的发展。还有必要对已引入责任概念的原因进行排序，找出与之最相关的原因。围绕责任计算出现了几个计算问题。国际和平研究所在这方面已经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仍然悬而未决。*在本提案中，我们讨论语义丰富的数据源上下文中的因果关系，目的是提出和研究基于本体的数据访问、虚拟数据集成和多维数据库等场景中的因果关系模型。我们还探讨了更基本的问题，例如原因的逻辑特征，这应该告诉我们嵌入数据源的理论可以用来推断原因以及如何推断原因。我们通过开发专门为数据库定制的机器学习方法来研究从(可能是概率的)数据源学习因果关系的问题，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.