A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling

Victor Hugo Silva-Blancas, Hugo Jiménez-Hernández, A. Herrera-Navarro, J. M. Álvarez-Alvarado, Diana-Margarita Córdova-Esparza, J. Rodríguez-Reséndíz
{"title":"A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling","authors":"Victor Hugo Silva-Blancas, Hugo Jiménez-Hernández, A. Herrera-Navarro, J. M. Álvarez-Alvarado, Diana-Margarita Córdova-Esparza, J. Rodríguez-Reséndíz","doi":"10.3390/computers13060149","DOIUrl":null,"url":null,"abstract":"With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: hk→H=SSE(C),wherek>0and∃X, executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems.","PeriodicalId":503381,"journal":{"name":"Computers","volume":" 17","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/computers13060149","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: hk→H=SSE(C),wherek>0and∃X, executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems.
基于聚类和 PL/SQL 的 MLP-Kmeans 建模评估方法
随着新的高性能服务器技术在数据中心和掩体中的应用,优化搜索引擎以高效处理时间和资源消耗是必要的。自 PL/SQL 出现以来,由标准 SQL 语言支撑的数据库查询系统一直保持着相同的功能设计。造成这种情况的原因是,最近的研究侧重于计算机资源管理、加密和安全,而不是改进基于人工智能工具、机器学习(ML)和人工神经网络(ANN)的数据挖掘。本研究提出了一种将多层感知器(MLP)与 Kmeans 相结合的预测方法。该方法与传统的 PL/SQL 工具进行了比较,旨在改善数据库响应时间,同时勾勒出 ML 和 Kmeans 在数据处理方面的未来优势。我们提出了一个新的推论:hk→H=SSE(C),其中k>0且∃X,在查询记录数超过 30.6 万条的数据集的应用软件上执行。这项研究根据行查询、组查询和总查询这三个假设,制作了 PL/SQL 和 MLP-Kmeans 的比较表。结果显示,行查询增加到 9 毫秒,组查询从 88 毫秒增加到 2460 毫秒,总查询从 13 毫秒增加到 279 毫秒。将一种方法与另一种方法进行对比测试,不仅显示了训练给数据库查询带来的增量疲劳和时间消耗,还显示了使用神经网络的复杂性能够产生比简单使用 PL/SQL 指令更精确的结果,这在未来特定领域的问题中将更为重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信