PUG：Why & Why-Not起源的框架和实际实现（扩展版）
PUG: A Framework and Practical Implementation for Why & Why-Not Provenance (extended version)
摘要：
解释为什么查询返回（或不返回）答案对于许多应用程序都很重要，包括审计、调试数据和查询以及回答关于数据的假设问题。在这项工作中，我们提出了第一个实用的方法来回答这些问题的查询与否定（一阶查询）。具体来说，我们介绍了一个基于图的出处模型，而在本质上的语法，支持反向推理，并证明编码广泛的出处模型从文献。该模型在我们的PUG（通过图形的起源统一）系统中的实现将起源问题和数据库查询作为输入，并生成计算解释的数据库程序，即，与回答问题相关的出处部分。此外，我们演示了如何一个理想的分解出处可以通过重写输入查询。我们实验评估我们的方法，证明其效率。
英文摘要：
Explaining why an answer is (or is not) returned by a query is important for many applications including auditing, debugging data and queries, and answering hypothetical questions about data. In this work, we present the first practical approach for answering such questions for queries with negation (first- order queries). Specifically, we introduce a graph-based provenance model that, while syntactic in nature, supports reverse reasoning and is proven to encode a wide range of provenance models from the literature. The implementation of this model in our PUG (Provenance Unification through Graphs) system takes a provenance question and Datalog query as an input and generates a Datalog program that computes an explanation, i.e., the part of the provenance that is relevant to answer the question. Furthermore, we demonstrate how a desirable factorization of provenance can be achieved by rewriting an input query. We experimentally evaluate our approach demonstrating its efficiency.
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