非确定性文档跨度器的恒定延迟枚举
Constant-Delay Enumeration for Nondeterministic Document Spanners
AI总结：
本文针对非确定性文档跨度器的信息提取，提出一种预处理线性、延迟恒定且与文档大小无关的枚举算法，实现组合复杂度可处理与最优数据复杂度，并通过实验验证。
摘要：
我们考虑称为文档跨度器的信息提取框架，并研究高效计算从输入文档中提取结果的问题，其中提取任务被描述为顺序变量集自动机（VA）。我们在枚举算法的设置中提出该问题，在此设置中，我们可以首先运行预处理阶段，然后必须在任意两个连续结果之间以较小的延迟产生结果。我们的目标是拥有一个在组合复杂度上可处理的算法，即输入文档和VA的大小；同时确保在输入文档大小方面具有最佳可能的数据复杂度界限，即文档大小上的恒定延迟。PODS'18的几项近期工作提出了此类算法，但存在文档大小上的线性延迟或对（通常非确定性的）输入VA大小的指数依赖。特别是，Florenzano等人认为我们期望的运行时保证无法满足一般顺序VA。我们反驳了这一点，并表明，给定非确定性顺序VA和输入文档，我们可以枚举VA在文档上的映射，具有以下界限：预处理在文档大小上是线性的，在VA大小上是多项式的，并且延迟独立于文档且在VA大小上是多项式的。由此产生的算法因此在组合复杂度上实现了可处理性，并具有最佳可能的数据复杂度界限。此外，它相当容易描述，特别是对于所谓的扩展VA的限制情况。最后，我们使用原型实现来经验性地评估我们的算法。
英文摘要：
We consider the information extraction framework known as document spanners, and study the problem of efficiently computing the results of the extraction from an input document, where the extraction task is described as a sequential variable-set automaton (VA). We pose this problem in the setting of enumeration algorithms, where we can first run a preprocessing phase and must then produce the results with a small delay between any two consecutive results. Our goal is to have an algorithm which is tractable in combined complexity, i.e., in the sizes of the input document and the VA; while ensuring the best possible data complexity bounds in the input document size, i.e., constant delay in the document size. Several recent works at PODS'18 proposed such algorithms but with linear delay in the document size or with an exponential dependency in size of the (generally nondeterministic) input VA. In particular, Florenzano et al. suggest that our desired runtime guarantees cannot be met for general sequential VAs. We refute this and show that, given a nondeterministic sequential VA and an input document, we can enumerate the mappings of the VA on the document with the following bounds: the preprocessing is linear in the document size and polynomial in the size of the VA, and the delay is independent of the document and polynomial in the size of the VA. The resulting algorithm thus achieves tractability in combined complexity and the best possible data complexity bounds. Moreover, it is rather easy to describe, in particular for the restricted case of so-called extended VAs. Finally, we evaluate our algorithm empirically using a prototype implementation.
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