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Articles by Ermeson Andrade
[2510.24188] Investigating Software Aging in LLM-Generated Software Systems
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Correction: Impacts of data consistency levels in cloud-based NoSQL for data-intensive applications - Journal of Cloud Computing
Correction: Impacts of data consistency levels in cloud-based NoSQL for data-intensive applications Correction Open access Published: 10 June 2025 Journal of Cloud Computing volume 14, Article number: 28 (2025) Cite this article The Original Article was published on 27 November 2024 Correction: J Cloud Comp 13, 158 (2024) https://doi.org/10.1186/s13677-024–00716-7 Following publication of the original article [1], the authors reported an error in Fig. 4 that should be corrected.
Correction to: Impacts of data consistency levels in cloud-based NoSQL for data-intensive applications - Journal of Cloud Computing
Correction Open access Published: 06 February 2025 Journal of Cloud Computing volume 14, Article number: 6 (2025) Cite this article The Original Article was published on 27 November 2024 Following publication of the original article [1], we have been notified that below-mentioned sentence in the body text should be deleted: It shows that ensuring strong data consistency in multi-cloud regions could decrease the response time of a DBMS up to 4,360%.
Experimental Performance Analysis of Data Consistency Levels in NoSQL Databases
Conflicts of Interest The authors declare no conflicts of interest. Supporting Information Filename Description spe3412-sup-0001-Supinfo.csvCSV document, 13 KB Data S1. Supporting Information. References 1 and , “Distributed and Parallel Database Systems,” ACM Computing Surveys 28, no. 1 (1996): 125–128. 2, , and , “Distributed Systems and Recent Innovations: Challenges and Benefits,” InfoNet Magazine 16, no. 3 (2006): 1–5. 3, , and , “Database Replication,” Synthesis Lectures on Data Management 5, no.
Impacts of data consistency levels in cloud-based NoSQL for data-intensive applications - Journal of Cloud Computing
Experiments results and discussion This section aims to present the results obtained from the experiments. Initially, we present the results related to throughput and response time under different consistency levels and workloads. After that, we examine resource utilization (CPU, memory) on the nodes to identify potential bottlenecks. Next, statistical analysis is conducted to confirm any differences in the results of the adopted metrics. Finally, we present a comparative analysis of our findings.
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