优化XML压缩
Optimizing XML Compression
摘要：
可扩展置标语言（XML）提供了一种强大而灵活的编码和交换数据的方法。事实证明，它作为一种编码格式的主要优点（即，它要求所有打开和关闭标记标签都存在并适当平衡）也产生了它的主要缺点之一：冗长。XML意识的压缩技术试图克服这个缺点。这些技术中有许多首先将XML结构与文档内容分离，然后分别压缩。通过识别高度相似的文档内容并将其压缩在一起，可以实现进一步的压缩增益，从而分摊所选压缩算法所需的辅助信息的存储成本。此外，压缩算法的正确选择不仅对可实现的压缩增益，而且对接入性能也是重要因素。因此，选择优化压缩增益的压缩配置需要确定（1）文档内容的分区策略，以及（2）应用于该分区内的每个集合的最佳可用压缩算法。在本文中，我们发现，找到一个最佳的压缩配置相对于压缩增益是一个NP-难的优化问题。这个问题仍然棘手，即使考虑一个单一的压缩算法的所有内容。我们还描述了一个近似算法的分支定界范式的基础上选择的文档内容的分区策略。
英文摘要：
The eXtensible Markup Language (XML) provides a powerful and flexible means of encoding and exchanging data. As it turns out, its main advantage as an encoding format (namely, its requirement that all open and close markup tags are present and properly balanced) yield also one of its main disadvantages: verbosity. XML-conscious compression techniques seek to overcome this drawback. Many of these techniques first separate XML structure from the document content, and then compress each independently. Further compression gains can be realized by identifying and compressing together document content that is highly similar, thereby amortizing the storage costs of auxiliary information required by the chosen compression algorithm. Additionally, the proper choice of compression algorithm is an important factor not only for the achievable compression gain, but also for access performance. Hence, choosing a compression configuration that optimizes compression gain requires one to determine (1) a partitioning strategy for document content, and (2) the best available compression algorithm to apply to each set within this partition. In this paper, we show that finding an optimal compression configuration with respect to compression gain is an NP-hard optimization problem. This problem remains intractable even if one considers a single compression algorithm for all content. We also describe an approximation algorithm for selecting a partitioning strategy for document content based on the branch-and-bound paradigm.
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