Abstract

Some of the most relevant document schemas used online, such as XML and JSON, have a nested format. In the past 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 Annotators (VPAnn), a computational model that extends visibly pushdown automata with outputs and has the same expressive power as monadic second-order logic over nested documents. Since processing a document through a VPAnn 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 article 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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