Reducing Symbol Search Overhead on Stream-Based Lossless Data Compression

摘要

Lossless data compression is emerged to utilize in the Big-Data 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 LC'T-DLT, and shows the performance evaluations to validate the effects of the method.