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

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

Received:
June 30, 2023
Accepted:
October 26, 2023
Published:
March 20, 2024
Keywords:
feature sequence rules, nearest neighbor, correlation matrix, storage engine, migration operation
Abstract

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:
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Last updated on Apr. 05, 2024