{'fraction_non_alphanumeric': 0.012997562956945572, 'fraction_numerical': 0.0, 'mean_word_length': 5.285714285714286, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Data compression and parallel computation model research under big data environment

In big data environment, data loss is a crucial issue which probably will occur due to the high network traffic, transmission delay and lesser bandwidth. This problem could be solved by adopting data compression schemes. These schemes could be classified into two types based on their actions: lossless compression and lossy compression. Lossy compression changes the output which will not be the same as input. Lossless compression changes the output and produces the output same as the input data. So the network overhead could be increased. The existing fixed and variable length coding technique have high robustness but poor efficiency. The efficiency problem can be solved by using the proposed scheme called “Data compression and parallel computation research model”. This proposed model uses a more sophisticated coding technique for the data compression and increases the efficiency while reducing the delay. Simulation results have shown that the proposed data compression and parallel computation research model has the better signal to noise ratio, increases the efficiency and reduces the delay when comparing to the existing models.