[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123446-en":3,"doc-seo-123446-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123446,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning for Optimizing Database Performance - Research Overview","Machine Learning for Optimizing Database Performance explores how machine learning workflows can address database security and efficiency challenges created by rapidly growing data storage needs. It outlines the ML pipeline from defining problems and preparing data, to modeling and system integration. The document reviews security solutions such as local deployment to reduce transport attacks, safer configuration handling, and traffic detection via a new system. It further covers efficiency methods for query filtering, sorting, scheduling acceleration, relationship discovery under queries, and highlights remaining practical constraints like reduced human effort and real-world limitations.","Machine Learning for Optimizing Database Performance  \nJiahui Ren  \nSchool of Statistics, Capital University of Economics and Business, Beijing, China  \nAbstract. Due to the developing requirement of data storage, today ’s database cannot meet the highly developing world. Besides, the wind of Machine Learning (ML) showed a possible solution to the dilemma.  \nTherefore, many researchers have proposed strategies to improve the performance of databases in terms of security and efficiency. This paper discusses the methods of how to use ML to improve the performance of databases. In the methodology part, an introduction is proposed to interpret the workflow of ML first. Then, security problems and relevant solutions arepresented in this paper, including improved performance to deploy the database locally to avoid attacks during data transportation, ignore dangerous configurations, and detect traffic data by building a new system.  \nBesides, efficiency issues are also resolved by ML. A strategy is introduced to filter dimensional queries, and sorting functions, to accelerate scheduling time which rapidly constructs the database, a framework and a method to find the relationships under queries also contributed. Lastly, the paper finds the underlying issues that are still under challenges. For instance, reduce manpower on building or managing a database, unified system, and realworld limitations.  \n1 Introduction  \nDatabase’s appearance shows the tremendous requirement for data storage because the advancing world may create countless data. Therefore, information could be stored through this kind of elaborate database. The database is an important scheme to store information in huge demand, as for the prosperity of the international market, there will be more demand for data storage. For instance, the database is a critical part of the Internet of Things (IoT) [1, 2]. By using data, people can transmit, connect, and use information [1, 2] .  \nMachine Learning (ML) is the technology that allows computers to learn the way of handling from data and algorithms and improve its ability to handle. ML has been developing for decades. It has a variety of practical applications. In a Coronavirus disease (COVID-19) investigation, machine learning plays a substantial role [3] . Besides, there is a popular topic about price prediction. When predicting Housing prices, Random Forest got the most accurate result, among Random Forest, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), which are methods of ML [4] . ML is also a way  \n[outlook_73731DB439C7F84F@outlook.com](outlook_73731DB439C7F84F@outlook.com)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nfor designers to help reduce costs for many aspects, like building construction, engine design, and optimizing the cost of computation in the cloud [5-7] .  \nThe fusion of machine learning and database management has already appeared [8] . It was also mentioned that previous database optimizing functions could not deal with the developing requirements, which created a larger-scale database [8] . Using this strategy to build databases will save designers from security issues and low efficiency. Multiple fields have already been using this function to help construct databases. For example, due to the development of New Energy Vehicles (NEVs), the investigation of batteries is increasing [9] . It was needed to find the hidden correlations between data, so using machine learning to accelerate the analysis process is a rational decision [9] . Besides, the solution to the dilemma of effectively storing data in the database is using traditional schemes like Deep Learning (DL), Reinforcement Learning (RL), Natural Language Processing (NLP), etc. [10] . These functions imply a po","cbCaigLGTeNQkz4F","https://ap.wps.com/l/cbCaigLGTeNQkz4F","pdf",337243,1,7,"English","en",105,"# Introduction\n# Method\n## Preliminaries of machine learning\n## Machine learning workflow and modeling","[{\"question\":\"What problems does the document aim to solve in database systems?\",\"answer\":\"It targets performance bottlenecks driven by growing data storage requirements, including both security risks and efficiency limitations in database management.\"},{\"question\":\"How is the machine learning workflow described in the methodology section?\",\"answer\":\"The workflow includes defining the project requirements and prediction goals, preparing and cleaning data, labeling for supervised learning, extracting variables, and training a model using a selected algorithm.\"},{\"question\":\"What efficiency techniques does the document mention for faster database operations?\",\"answer\":\"It discusses filtering dimensional queries and sorting functions to accelerate scheduling time, and using a framework to identify underlying relationships within queries.\"}]","Machine Learning for Optimizing Database Performance - 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