用于文档跨度器的 Skyline 算子
Skyline Operators for Document Spanners
AI总结：
本文为 document spanners 引入 skyline 算子以将支配过滤编译进提取过程，并刻画其正则可表达性、状态膨胀及组合复杂度的可处理与不可处理边界。
摘要：
从文本文档中提取跨度（区间）关系时，一种常见做法是过滤掉关系中被认为受其他元组支配的元组。支配规则被定义为一种偏序，并随不同系统和任务而变化。例如，我们可以规定一个元组受那些通过赋予额外属性或赋予更大区间来扩展它的元组支配。于是，过滤该关系的结果将是根据该偏序得到的 skyline。由于这种过滤可能会移除大多数已提取元组，我们研究是否可以通过将支配规则编译到提取器中来提高提取性能。为此，我们针对以 document spanners（文档跨度器）表示的声明式信息提取任务引入了 skyline 算子。我们证明，当支配偏序本身可以表示为正则跨度器时，该算子可以通过正则运算来表示，这涵盖了若干自然的支配规则。然而，我们也证明 skyline 算子会带来计算代价（在组合复杂度下）。首先，在某些情况下，将跨度器表示为顺序变量集自动机所需的状态数会发生指数级膨胀。其次，求值可能在计算上变得困难。我们的分析更精确地识别了组合复杂度可处理或不可处理的支配规则类别。
英文摘要：
When extracting a relation of spans (intervals) from a text document, a common practice is to filter out tuples of the relation that are deemed dominated by others. The domination rule is defined as a partial order that varies along different systems and tasks. For example, we may state that a tuple is dominated by tuples which extend it by assigning additional attributes, or assigning larger intervals. The result of filtering the relation would then be the skyline according to this partial order. As this filtering may remove most of the extracted tuples, we study whether we can improve the performance of the extraction by compiling the domination rule into the extractor. To this aim, we introduce the skyline operator for declarative information extraction tasks expressed as document spanners. We show that this operator can be expressed via regular operations when the domination partial order can itself be expressed as a regular spanner, which covers several natural domination rules. Yet, we show that the skyline operator incurs a computational cost (under combined complexity). First, there are cases where the operator requires an exponential blowup on the number of states needed to represent the spanner as a sequential variable-set automaton. Second, the evaluation may become computationally hard. Our analysis more precisely identifies classes of domination rules for which the combined complexity is tractable or intractable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
