Enhancing performance of E-Government information systems with SSD-based Hadoop mapreduce
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| Publicado no: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 33921-33936 |
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Nature Publishing Group
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| Acesso em linha: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Ishengoma, Fredrick |u Department of Information Systems and Technology, College of Informatics and Virtual Education, The University of Dodoma, Dodoma, Tanzania (ROR: https://ror.org/009n8zh45) (GRID: grid.442459.a) (ISNI: 0000 0001 1998 2954) | |
| 245 | 1 | |a Enhancing performance of E-Government information systems with SSD-based Hadoop mapreduce | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a E-government applications generate and process large volumes of heterogeneous data that demand high-throughput and low-latency computation. Although Hadoop MapReduce is commonly used for such tasks, its performance is often limited by disk I/O constraints and network delays during the shuffle phase. This study proposes a data address-based shuffle mechanism optimized for Hadoop clusters equipped with Solid-State Drives (SSDs), aiming to enhance data processing performance in e-government applications. The mechanism introduces three key components: address-based sorting, address-based merging, and pre-transmission of intermediate data, which collectively reduce disk I/O and network transfer overhead. Experimental evaluations using Terasort and Wordcount benchmarks demonstrate execution time reductions of 8% and 1%, respectively, with statistical significance confirmed through 95% confidence intervals. Scalability assessments on a simulated 50-node cluster and energy profiling further validate the approach, showing improved performance, reduced network congestion, and a 31% decrease in energy consumption compared to HDD-based systems. The findings establish the proposed mechanism as a cost-effective and efficient solution for large-scale data processing in public sector computing environments. | |
| 653 | |a Energy consumption | ||
| 653 | |a Big Data | ||
| 653 | |a Machine learning | ||
| 653 | |a Benchmarks | ||
| 653 | |a Government information | ||
| 653 | |a Network management systems | ||
| 653 | |a Information systems | ||
| 653 | |a Data processing | ||
| 653 | |a Electronic government | ||
| 653 | |a Public sector | ||
| 653 | |a Literature reviews | ||
| 653 | |a Batch processing | ||
| 653 | |a Latency | ||
| 653 | |a Fault tolerance | ||
| 653 | |a Efficiency | ||
| 653 | |a Distributed processing | ||
| 653 | |a Economic | ||
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 33921-33936 | |
| 786 | 0 | |d ProQuest |t Science Database | |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3256002334/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |