使用外部内存验证流模型中的XML文档

IF 2.2 2区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
C. Konrad, F. Magniez
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引用次数: 1

摘要

我们研究了在流算法上下文中针对一般dtd验证大小为N的XML文档的问题。这项工作的出发点是一个众所周知的空间下界。有些XML文档和dtd的p-pass流算法需要Ω(N/p)空间。我们展示了当允许访问外部内存时,有一种确定性流算法可以用内存空间&Order;(log2 N)、一个常数的辅助读/写流和&Order;(log N)传递XML文档和辅助流的总次数来解决这个问题。该算法的一个重要中间步骤是以流方式计算初始XML文档的第一子-下一兄弟(FCNS)编码。我们独立地研究了这个问题,并且我们还提供了用于解码以FCNS编码给出的XML文档的内存高效流算法。此外,在没有外部内存的情况下,根据常规流模型中的任何DTD验证编码二叉树的XML文档,可以使用次线性内存来完成。有一种使用&Order;(√N log N)空间的单遍算法,以及一种使用&Order;(log2n)空间的双向双遍算法来执行此任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validating XML documents in the streaming model with external memory
We study the problem of validating XML documents of size N against general DTDs in the context of streaming algorithms. The starting point of this work is a well-known space lower bound. There are XML documents and DTDs for which p-pass streaming algorithms require Ω(N/p) space. We show that when allowing access to external memory, there is a deterministic streaming algorithm that solves this problem with memory space &Order;(log2 N), a constant number of auxiliary read/write streams, and &Order;(log N) total number of passes on the XML document and auxiliary streams. An important intermediate step of this algorithm is the computation of the First-Child-Next-Sibling (FCNS) encoding of the initial XML document in a streaming fashion. We study this problem independently, and we also provide memory-efficient streaming algorithms for decoding an XML document given in its FCNS encoding. Furthermore, validating XML documents encoding binary trees against any DTD in the usual streaming model without external memory can be done with sublinear memory. There is a one-pass algorithm using &Order;(√N log N) space, and a bidirectional two-pass algorithm using &Order;(log2 N) space which perform this task.
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来源期刊
ACM Transactions on Database Systems
ACM Transactions on Database Systems 工程技术-计算机:软件工程
CiteScore
5.60
自引率
0.00%
发文量
15
审稿时长
>12 weeks
期刊介绍: Heavily used in both academic and corporate R&D settings, ACM Transactions on Database Systems (TODS) is a key publication for computer scientists working in data abstraction, data modeling, and designing data management systems. Topics include storage and retrieval, transaction management, distributed and federated databases, semantics of data, intelligent databases, and operations and algorithms relating to these areas. In this rapidly changing field, TODS provides insights into the thoughts of the best minds in database R&D.
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