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We present D-v2v, a new dynamic (one-pass) variable-to-variable compressor. Variable-tovariable compression aims at using a modeler that gathers variable-length input symbols and a variable-length statistical coder that assigns shorter codewords to the more frequent symbols. In D-v2v, we process the input text word-wise to gather variable-length symbols that can be either terminals (new words) or non-terminals, subsequences of words seen before in the input text. Those input symbols are set in a vocabulary that is kept sorted by frequency. Therefore, those symbols can be easily encoded with dense codes. Our D-v2v permits real-time transmission of data, i.e. compression/transmission can begin as soon as data become available. Our experiments show that D-v2v is able to overcome the compression ratios of the v2vDC, the state-of-the-art semi-static variable-to-variable compressor, and to almost reach p7zip values. It also draws a competitive performance at both compression and decompression.
We present D-v2v, a new dynamic (one-pass) variable-to-variable compressor. Variable-tovariable compression aims at using a modeler that gathers variable-length input symbols and a variable-length statistical coder that assigns shorter codewords to the more frequent symbols. In D-v2v, we process the input text word-wise to gather variable-length symbols that can be either terminals (new words) or non-terminals, subsequences of words seen before in the input text. Those input symbols are set in a vocabulary that is kept sorted by frequency. Therefore, those symbols can be easily encoded with dense codes. Our D-v2v permits real-time transmission of data, i.e. compression/transmission can begin as soon as data become available. Our experiments show that D-v2v is able to overcome the compression ratios of the v2vDC, the state-of-the-art semi-static variable-to-variable compressor, and to almost reach p7zip values. It also draws a competitive performance at both compression and decompression.
We address the problem of adaptive compression of natural language text, considering the case where the receiver is much less powerful than the sender, as in mobile applications. Our techniques achieve compression ratios around 32% and require very little effort from the receiver. Furthermore, the receiver is not only lighter, but it can also search the compressed text with less work than the necessary to uncompress it. This is a novelty in two senses: it breaks the usual compressor/decompressor symmetry typical of adaptive schemes, and it contradicts the long-standing assumption that only semistatic codes could be searched more efficiently than the uncompressed text. Our novel compression methods are in several aspects preferable over the existing adaptive and semistatic compressors for natural language texts.
Advances in technology and economical pressure have forced many organizations to consider the migration of their legacy systems to newer platforms. Legacy systems typically provide mission critical services vital for an organization's business needs. These systems are usually very large and highly complex with little or no documentation. Furthermore, fewer people can understand and maintain these systems. While several techniques exist to verify the functionality of the migrated system, the literature is still lacking methods to effectively assess the performance impact of software migration. In this paper, we propose a new method designed specifically to address performance evaluation in software migration projects. The new method uses simple models and incorporates techniques for model validation and resource demand mapping for performance evaluation and capacity planning.
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