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Evaluating Aggregate Functions and Machine Learning Integration: A Comparative Analysis of Performance, Security, and NoSQL Connectivity in Oracle, SQL Server, and MySQL.

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Title: Evaluating Aggregate Functions and Machine Learning Integration: A Comparative Analysis of Performance, Security, and NoSQL Connectivity in Oracle, SQL Server, and MySQL.
Authors: Hussein, Dana Lattef
Source: UHD Journal of Science & Technology; Jul2024, Vol. 8 Issue 2, p7-23, 17p
Subject Terms: MACHINE learning, NONRELATIONAL databases, INDEXING, ACQUISITION of data, DEEP learning
Abstract: This paper is a comparison study on aggregate functions and windows function between the three major Relational Database Management Systems (RDBMSs): Oracle, SQL Server, and MySQL. These functions are essential to handle a huge data set and prepare it for effective analysis. The research is conducted to analyse the performance of these systems, their utilization of resources, while executing aggregate queries. Further, this paper examines the integration of machinelearning abilities and NoSQL database connectivity within these platforms. All these were measured under a constant benchmarking framework. It also discusses the analysis on how indexing affects query performance and the integration of machine-learning (ML) models with these databases. The results are indicative of considerable performance variation, resource efficiency, and ML integration among the three RDBMSs. Oracle is the best solution for implementing complex aggregations and ML integration, making it the best alternative to work on large datasets. Where MySQL is very efficient for most simple tasks, it lacks advanced features and does not have native ML support. It further provides optimization strategies for each RDBMS and gives insight into securing data and integrating with NoSQL databases. This research is set out to guide database administrators and developers in choosing the most appropriate RDBMS in relation to their specific needs in aggregation, ML, NoSQL integration. However, the factor of indexing is generally what brought most success to query optimization in these databases: Oracle, SQL Server, and MySQL. Among these, Oracle still was significantly outdoing both others, which further improved by indexing. In general, MySQL was less performant and lacked some functionality in window functions. Aggregation queries seem to profit more from indexing, but the less improvement was seen for window functions (STRING_AGG). All in all, indexing is a very effective technique in optimizing query efficiency. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:This paper is a comparison study on aggregate functions and windows function between the three major Relational Database Management Systems (RDBMSs): Oracle, SQL Server, and MySQL. These functions are essential to handle a huge data set and prepare it for effective analysis. The research is conducted to analyse the performance of these systems, their utilization of resources, while executing aggregate queries. Further, this paper examines the integration of machinelearning abilities and NoSQL database connectivity within these platforms. All these were measured under a constant benchmarking framework. It also discusses the analysis on how indexing affects query performance and the integration of machine-learning (ML) models with these databases. The results are indicative of considerable performance variation, resource efficiency, and ML integration among the three RDBMSs. Oracle is the best solution for implementing complex aggregations and ML integration, making it the best alternative to work on large datasets. Where MySQL is very efficient for most simple tasks, it lacks advanced features and does not have native ML support. It further provides optimization strategies for each RDBMS and gives insight into securing data and integrating with NoSQL databases. This research is set out to guide database administrators and developers in choosing the most appropriate RDBMS in relation to their specific needs in aggregation, ML, NoSQL integration. However, the factor of indexing is generally what brought most success to query optimization in these databases: Oracle, SQL Server, and MySQL. Among these, Oracle still was significantly outdoing both others, which further improved by indexing. In general, MySQL was less performant and lacked some functionality in window functions. Aggregation queries seem to profit more from indexing, but the less improvement was seen for window functions (STRING_AGG). All in all, indexing is a very effective technique in optimizing query efficiency. [ABSTRACT FROM AUTHOR]
ISSN:25214209
DOI:10.21928/uhdjst.v8n2y2024.pp7-23