JACIII Vol.28 No.2 pp. 316-323
doi: 10.20965/jaciii.2024.p0316

Research Paper:

Time Series Storage Method of Multi-Valued Attribute Data in Energy Big Data Center

Peng Chen*,† and Dongge Zhu**

*State Grid Ningxia Electric Power Co., Ltd.
288 Changcheng East Road, Xingqing District, Yinchuan, Ningxia 750001, China

Corresponding author

**Electric Power Research Institute, State Grid Ningxia Electric Power Co., Ltd.
716 Huanghe East Road, Jinfeng District, Yinchuan, Ningxia 750002, China

June 30, 2023
October 26, 2023
March 20, 2024
feature sequence rules, nearest neighbor, correlation matrix, storage engine, migration operation

Herein, the time series storage method of multi-valued attribute data, aimed at improving the efficiency of writing and querying data in “energy” big data centers is reported. Through rule construction and rule iteration of feature sequence, the time series features of multi-valued attribute data are extracted, and the “component attribute nearest neighbor propagation method” is used for clustering; the data are divided into cold, warm, and hot data. A storage engine with a solid state disk layer, mechanical hard disk layer, and memory layer has been designed, and the efficiency of data writing and query through migration operation is improved. The experimental results demonstrate that this method can effectively extract the time series features of multi-valued attribute data, and the Pre value of the clustering time series is higher than 0.93, which effectively improves the data writing and query efficiency of the energy big data center.

Cite this article as:
P. Chen and D. Zhu, “Time Series Storage Method of Multi-Valued Attribute Data in Energy Big Data Center,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.2, pp. 316-323, 2024.
Data files:
  1. [1] P. Yang et al., “Parallel permutation entropy feature extraction method for time series data based on cloud platform,” Electric Power Automation Equipment, Vol.39, No.4, pp. 217-223, 2019 (in Chinese).
  2. [2] J. Li et al., “Research on big data acquisition and application of power energy based on big data cloud platform,” Electrical Measurement & Instrumentation, Vol.56, No.12, pp. 104-109, 2019 (in Chinese).
  3. [3] X. Pan et al., “Research and application survey of similarity measurement methods on trajectory data based on time series,” J. of Yanshan University, Vol.43, No.6, pp. 531-545, 2019 (in Chinese).
  4. [4] C. Deng et al., “Outlier detection method based on multivariate time series segmentation clustering,” Computer Engineering and Design, Vol.2020, No.11, pp. 131-136, 2020 (in Chinese).
  5. [5] T. Gao et al., “A massively parallel Bayesian approach to factorization-based analysis of big time series data,” J. of Computer Research and Development, Vol.56, No.7, pp. 1567-1577, 2019 (in Chinese).
  6. [6] D. Saxena and A. K. Singh, “A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center,” Neurocomputing, Vol.426, pp. 248-264, 2021.
  7. [7] B. He, “Algorithm to enhance security of dynamic data migration in optical fiber network,” Wireless Personal Communications, Vol.127, No.2, pp. 1331-1339, 2022.
  8. [8] H. Chen et al., “A dynamic density clustering algorithm for time series data,” Control Theory & Applications, Vol.36, No.8, pp. 1304-1314, 2019 (in Chinese).
  9. [9] Y. Huang et al., “Research on modeling method of medium- and long-term wind power time series based on K-means MCMC algorithm,” Power System Technology, Vol.43, No.7, pp. 2469-2476, 2019 (in Chinese).
  10. [10] J. Wang et al., “Time series modeling method for multi-photovoltaic power stations considering spatial correlation and weather type classification,” Power System Technology, Vol.44, No.4, pp. 1376-1384, 2020 (in Chinese).
  11. [11] R.-C. Xu et al., “Time series forecasting based on seasonality modeling and its application to electricity price forecasting,” Acta Automatica Sinica, Vol.46, No.6, pp. 1136-1144, 2020 (in Chinese).
  12. [12] P. Liu et al., “Energy internet data protection based on attribute based hidden access strategy,” Computer Engineering & Science, Vol.41, No.4, pp. 649-656, 2019 (in Chinese).
  13. [13] Y. Zuo et al., “Prediction method of multivariate time series based on ensemble learning,” J. of Chinese Computer Systems, Vol.41, No.12, pp. 2475-2479, 2020 (in Chinese).
  14. [14] H. Li, C. Wang, and X. Deng, “Multivariate time series clustering based on affinity propagation of component attributes,” Control and Decision, Vol.33, No.4, pp. 649-656, 2018 (in Chinese).
  15. [15] W.-T. Mao et al., “Structural prediction of multivariate time series through outlier elimination,” Acta Automatica Sinica, Vol.44, No.4, pp. 619-634, 2018 (in Chinese).
  16. [16] Z. Li et al., “Method of missing data imputation for multivariate time series,” Systems Engineering and Electronics, Vol.40, No.1, pp. 225-230, 2018 (in Chinese).
  17. [17] M. Zhang and Y. Xu, “Personalized privacy protection method for data with multiple numerical sensitive attributes,” J. of Computer Applications, Vol.40, No.2, pp. 491-496, 2020 (in Chinese).

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Last updated on Apr. 05, 2024