Streaming Enumeration on Nested Documents

Martin Muñoz, Cristian Riveros
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引用次数: 4

Abstract

Some of the most relevant document schemas used online, such as XML and JSON, have a nested format. In the last decade, the task of extracting data from nested documents over streams has become especially relevant. We focus on the streaming evaluation of queries with outputs of varied sizes over nested documents. We model queries of this kind as Visibly Pushdown Transducers (VPT), a computational model that extends visibly pushdown automata with outputs and has the same expressive power as MSO over nested documents. Since processing a document through a VPT can generate a massive number of results, we are interested in reading the input in a streaming fashion and enumerating the outputs one after another as efficiently as possible, namely, with constant-delay. This paper presents an algorithm that enumerates these elements with constant-delay after processing the document stream in a single pass. Furthermore, we show that this algorithm is worst-case optimal in terms of update-time per symbol and memory usage.
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嵌套文档上的流式枚举
在线使用的一些最相关的文档模式(如XML和JSON)具有嵌套格式。在过去十年中,从流上的嵌套文档中提取数据的任务变得尤为重要。我们关注的是在嵌套文档上对具有不同大小输出的查询进行流计算。我们将这种查询建模为可见下推换能器(VPT),这是一种计算模型,它扩展了具有输出的可见下推自动机,并且具有与嵌套文档上的MSO相同的表达能力。由于通过VPT处理文档可以生成大量结果,因此我们希望以流方式读取输入,并尽可能高效地枚举输出,即使用恒定延迟。本文提出了一种算法,该算法在单遍处理文档流后以恒定延迟枚举这些元素。此外,我们证明了该算法在每个符号的更新时间和内存使用方面是最坏情况下的最优算法。
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