Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (7): 2109-2114.doi: 10.13229/j.cnki.jdxbgxb.20220348

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Text data compression and storage algorithm based on time series model

Yuan-han WENG1,2(),Nan LI1()   

  1. 1.College of Economics and Management，Nanjing University of Aeronautics and Astronautics，Nanjing 211000，China
    2.School of Economics and Management，Nanjing Technology University，Nanjing 211800，China
  • Received:2022-03-31 Online:2023-07-01 Published:2023-07-20
  • Contact: Nan LI E-mail:wengyuanhan2022@163.com;linan20220202@163.com

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Abstract:

In order to reduce the amount of historical data of text data and improve the efficiency of text data compression and storage， a text data compression and storage algorithm based on time series model is proposed. The wavelet threshold denoising method is used to estimate and eliminate the error and noise of text data； from the perspective of text data features， the features are described in detail， and the combination and inheritance relationship between feature types are set to build a time series model. Convert the preprocessed text data into binary coded bytes with similar structure using the time series model， perform XOR operation to compress the redundant part in the result， and store the compressed data in the corresponding database， and finally complete the text Data compression storage. The simulation results show that the proposed algorithm can effectively improve the compression performance and obtain more satisfactory compression and storage results of text data.

Key words: time series model, text data, compressed storage algorithm, wavelet threshold denoising method, nonlinear function, pretreatment

CLC Number: 

  • TP393

Fig.1

Flow chart of wavelet threshold denoising"

Fig.2

Flow chart of text data compression based ontime series model"

Fig.3

Comparison of text data compression perfor-mance test results of different algorithms"

Table 1

Comparison of network average energy consumption test results of different algorithms"

Running time/min网络平均能耗/J
本文方法文献［3］ 方法文献［4］方法
600.250.280.32
1200.320.360.40
1800.360.410.45
2400.400.460.50
3000.440.490.54
3600.470.530.57
4200.530.580.62
4800.580.630.68
5400.620.680.75
6000.670.720.79

Fig.4

Comparison of network average delay test results of different algorithms"

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