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<p>At present, the daily operation and maintenance of large-scale data centers such as banks in China, due to a variety of reasons, often brings about the problem of unexpected events that are difficult to locate. In order to ensure that the systems running in the data center work efficiently, this paper proposes a method for improving the operation, maintenance, and monitoring of financial data centers based on the cloud-native model. First, we sequentially cleanse and process the financial center data to eliminate any negative impact and generate a time-trending correlation of financial attributes. We then apply association mining to data center operation and maintenance, using stock information as an example to analyze the operational results in stock trading transactions. The result of correlation mining is component B index (up)⇒ component A index (up), support = 12/100, confidence = 12/19, which indicates that in 100 trading days, the number of days that the component B index and the component A index rise together is 12 days, while the number of days that the component B rises alone is 19 days. In the case study examining the impact of association mining in stock trading, on March 15, 2022, the stock price experienced a rise from 11.456 to 11.498 within a mere 0.1s. The financial data operation and maintenance system, using association mining, identified this as “abnormal,” demonstrating the model’s successful detection of abnormal behavior.</p>
</abstract>

At present, the daily operation and maintenance of large-scale data centers such as banks in China, due to a variety of reasons, often brings about the problem of unexpected events that are difficult to locate. In order to ensure that the systems running in the data center work efficiently, this paper proposes a method for improving the operation, maintenance, and monitoring of financial data centers based on the cloud-native model. First, we sequentially cleanse and process the financial center data to eliminate any negative impact and generate a time-trending correlation of financial attributes. We then apply association mining to data center operation and maintenance, using stock information as an example to analyze the operational results in stock trading transactions. The result of correlation mining is component B index (up)⇒ component A index (up), support = 12/100, confidence = 12/19, which indicates that in 100 trading days, the number of days that the component B index and the component A index rise together is 12 days, while the number of days that the component B rises alone is 19 days. In the case study examining the impact of association mining in stock trading, on March 15, 2022, the stock price experienced a rise from 11.456 to 11.498 within a mere 0.1s. The financial data operation and maintenance system, using association mining, identified this as “abnormal,” demonstrating the model’s successful detection of abnormal behavior.

Correlation Mining-Based Strategies for Improving the Quality and Efficiency of Financial Data Center Operation, Maintenance, and Monitoring in Cloud-Native Models

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

At present, the daily operation and maintenance of large-scale data centers such as banks in China, due to a variety of reasons, often brings about the problem...

Correlation Mining-Based Strategies for Improving the Quality and Efficiency of Financial Data Center Operation, Maintenance, and Monitoring in Cloud-Native Models

Publié en ligne: 05 août 2024

Pages: -

Reçu: 13 avr. 2024

Accepté: 30 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2198

Mots clés
Association Mining, Data Cleaning, Financial Data Center, Abnormal Behavior Detection

© 2024 Kun Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Correlation Mining-Based Strategies for Improving the Quality and Efficiency of Financial Data Center Operation, Maintenance, and Monitoring in Cloud-Native Models

Publié en ligne: 05 août 2024

Pages: -

Reçu: 13 avr. 2024

Accepté: 30 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2198

Mots clésAssociation Mining, Data Cleaning, Financial Data Center, Abnormal Behavior Detection

© 2024 Kun Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Association Mining, Data Cleaning, Financial Data Center, Abnormal Behavior Detection