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Published in:
2019 | OriginalPaper | Chapter
Reducing Symbol Search Overhead on Stream-Based Lossless Data Compression
Authors : Shinichi Yamagiwa, Ryuta Morita, Koichi Marumo
Published in: Computational Science – ICCS 2019
Publisher: Springer International Publishing
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Abstract
Lossless data compression is emerged to utilize in the BigData applications in the recent days. The conventional algorithms mainly generate a symbol lookup table to replace a frequent data pattern in the inputted data to a symbol, and then compresses the information. This kind of the dictionary-based compression mechanism potentially has an overhead problem regarding the number of symbol matchings in the table. This paper focuses on a novel method to reduce the number of searches in the table using a bank separation technique. This paper reports design and implementation of the bank select method on the LCT-DLT, and shows the performance evaluations to validate the effects of the method.