Datalog in Wonderland
Datalog in Wonderland
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DOI：
10.1145/3552490.3552492
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Mahmoud Abo Khamis;RelationalAI;H. Ngo;R. Pichler;T. Wien;Dan Suciu
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文献类型：
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作者：
Mahmoud Abo Khamis;RelationalAI;H. Ngo;R. Pichler;T. Wien;Dan Suciu
现代数据分析应用程序（例如知识图推理和机器学习）通常涉及递归通过汇总，对系统构建者和理论家构成了巨大的挑战：首先，为这些计算提供简单而强大的抽象；抽象的语义是为这些计算设计的优化技术。解决这些挑战是一个简单的抽象，它允许聚合与递归交织在一起，并保留大量的简单性和优雅，我们定义了其正式的语义。通过几个示例，数据可以用于多种应用程序。 FGH规则，然后在几个示例上说明了FGH规则，包括简单的魔术重写，广义的半遗产评估和一个物质示例，并简要讨论了FGH规则的实现，并介绍了一些实验验证其有效性。
Modern data analytics applications, such as knowledge graph reasoning and machine learning, typically involve recursion through aggregation. Such computations pose great challenges to both system builders and theoreticians: first, to derive simple yet powerful abstractions for these computations; second, to define and study the semantics for the abstractions; third, to devise optimization techniques for these computations. In recent work we presented a generalization of Datalog called Datalog, which addresses these challenges. Datalog is a simple abstraction, which allows aggregates to be interleaved with recursion, and retains much of the simplicity and elegance of Datalog. We define its formal semantics based on an algebraic structure called Partially Ordered Pre-Semirings, and illustrate through several examples how Datalog can be used for a variety of applications. Finally, we describe a new optimization rule for Datalog, called the FGH-rule, then illustrate the FGH-rule on several examples, including a simple magic-set rewriting, generalized semi-naïve evaluation, and a bill-of-material example, and briefly discuss the implementation of the FGH-rule and present some experimental validation of its effectiveness.