HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach
HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach
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DOI：
10.1145/3514221.3526149
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发表时间：
2022-03
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影响因子：
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通讯作者：
Sainyam Galhotra;Amir Gilad;Sudeepa Roy;Babak Salimi
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文献类型：
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作者：
Sainyam Galhotra;Amir Gilad;Sudeepa Roy;Babak Salimi
假设分析（为数据库更新提供支持）和方法分析（如何修改数据库以实现目标）为希望检查假设场景而不对数据库进行实际更改的用户提供了见解，从而帮助他们在自己的领域规划战略。通常，此类分析是通过测试现有数据库中的更新对由感兴趣的查询创建的特定视图的影响来完成的。然而，在现实场景中，由于隐含的语义依赖性，对数据库特定部分的更新可能会影响完全不同部分中的元组和属性。为了允许假设推理，同时适应这种依赖关系，我们开发HypeR，一个框架，支持什么，如果和如何查询占概率因果模型捕获的属性之间的概率依赖关系。我们扩展的SQL语法，包括必要的运营商表示这些假设的查询，定义它们的语义，设计有效的算法和优化计算结果使用的概念因果关系和概率数据库，并评估我们的方法的有效性实验。
What-if (provisioning for an update to a database) and how-to (how to modify the database to achieve a goal) analyses provide insights to users who wish to examine hypothetical scenarios without making actual changes to a database and thereby help plan strategies in their fields. Typically, such analyses are done by testing the effect of an update in the existing database on a specific view created by a query of interest. In real-world scenarios, however, an update to a particular part of the database may affect tuples and attributes in a completely different part due to implicit semantic dependencies. To allow for hypothetical reasoning while accommodating such dependencies, we develop HypeR, a framework that supports what-if and how-to queries accounting for probabilistic dependencies among attributes captured by a probabilistic causal model. We extend the SQL syntax to include the necessary operators for expressing these hypothetical queries, define their semantics, devise efficient algorithms and optimizations to compute their results using concepts from causality and probabilistic databases, and evaluate the effectiveness of our approach experimentally.