基于模型的一致性压缩

A. Bookstein, S. T. Klein, T. Raita
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引用次数: 15

摘要

作者使用压缩理论中常用的框架来讨论一致性压缩。他们首先创建一个一致性生成的数学模型,然后使用最优压缩引擎,如霍夫曼或算术编码,来进行实际的压缩。需要注意的是,在静态信息检索系统中,压缩和解压缩不是对称的任务。在构建系统时,压缩只执行一次,而在处理每个查询期间都需要解压缩,并直接影响响应时间。因此,只要有合理快速的解压缩方法,就可以对压缩使用广泛而昂贵的预处理。此外,压缩应用于整个文件(文本、一致性等),但只需要对(可能很多)短文件进行解压缩,这些文件可以通过指向其确切位置的指针随机访问。因此,排除了使用基于表的自适应方法,这些表从开始到结束系统地更改文件。然而,他们关心的不是编码或解码的速度,而是将一致性压缩概念与现代数据压缩方法联系起来,并测试其模型的有效性。
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Model based concordance compression
The authors discuss concordance compression using the framework now customary in compression theory. They begin by creating a mathematical model of concordance generation, and then use optimal compression engines, such as Huffman or arithmetic coding, to do the actual compression. It should be noted that in the context of a static information retrieval system, compression and decompression are not symmetrical tasks. Compression is done only once, while building the system, whereas decompression is needed during the processing of every query and directly affects the response time. One may thus use extensive and costly preprocessing for compression, provided reasonably fast decompression methods are possible. Moreover, compression is applied to the full files (text, concordance, etc.), but decompression is needed only for (possibly many) short pieces, which may be accessed at random by means of pointers to their exact locations. Therefore the use of adaptive methods based on tables that systematically change from the beginning to the end of the file is ruled out. However, their concern is less the speed of encoding or decoding than relating concordance compression conceptually to the modern approach of data compression, and testing the effectiveness of their models.<>
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