[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127102-en":3,"doc-seo-127102-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127102,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Utilizing Machine Learning for Automated Data Normalization in Supermarket Sales Databases","Data normalization is a crucial step in database management systems (DBMS), ensuring consistency, minimizing redundancy, and improving query performance. In supermarket sales databases, traditional normalization often requires extensive manual effort and strong domain expertise, leading to delays and error-prone outcomes. This paper presents an ML-based framework that automates normalization by discovering functional dependencies, detecting anomalies, and recommending schema transformations. Experiments on supermarket sales datasets demonstrate gains in accuracy, scalability, and processing time, supporting more efficient operations and better decision-making.","International Journal of Advanced  \nResearch in Education and  \nTechnologY (IJARETY)  \n[www.ijarety.in](www.ijarety.in)  editor.ijarety@gmail.com  \nInternational Journal of Advanced Research in Education and TechnologY(IJARETY)  \n| ISSN: [2394-2975 | ](2394-2975 | www.ijarety.in| A Bi-Monthly)[www.ijarety.in](2394-2975 | www.ijarety.in| A Bi-Monthly)[| A Bi-Monthly](2394-2975 | www.ijarety.in| A Bi-Monthly), Double-Blind Peer Reviewed & Referred Journal |  \n|| Volume 10, Issue 1, January-February 2023 ||  \nDOI:10.15680/IJARETY.2023.1001002  \nUtilizing Machine Learning for Automated Data Normalization in Supermarket Sales Databases  \nVimal Raja Gopinathan  \nSenior Principal Consultant, Oracle Financial Services Software Ltd., Bengaluru, India  \nABSTRACT: Data normalization is a crucial step in database management systems (DBMS), ensuring consistency, minimizing redundancy, and enhancing query performance. Traditional methods of normalization in supermarket sales databases often demand significant manual effort and domain expertise, making the process time-consuming and prone to errors. This paper introduces an innovative machine learning (ML)-based framework to automate data normalization in supermarket sales databases. The proposed approach utilizes both supervised and unsupervised ML techniques to identify functional dependencies, detect anomalies, and suggest optimal schema transformations. Experiments on supermarket sales datasets show substantial improvements in accuracy, scalability, and processing time compared to traditional approaches. The results emphasize the potential of incorporating ML into database management practices to boost operational efficiency and support better decision-making.  \nKEYWORDS: Data normalization, machine learning, supermarket sales, functional dependency, anomaly detection, schema transformation, database management, reinforcement learning.  \nI. INTRODUCTION  \nData normalization is a critical operation in relational database management systems (RDBMS), aimed at organizing data to eliminate redundancy and maintain integrity. Supermarket sales databases, which store vast amounts of transactional data, often require extensive normalization to meet business requirements and optimize query performance. Manual normalization, however, is labor-intensive, prone to human errors, and challenging to scale for large datasets. Advances in machine learning provide opportunities to automate this process, enabling efficient detection of functional dependencies and schema optimization.  \nThis paper proposes a machine learning-based framework to automate data normalization tasks in supermarket sales databases. By leveraging ML algorithms, the system reduces human intervention, accelerates schema design, and enhances database performance. The following sections detail the methodology, experimental results, and potential applications of the framework.  \nII. LITERATURE SURVEY  \nExtensive research has been conducted in the field of data normalization and database management. Traditional techniques rely heavily on manual processes and rule-based algorithms to identify functional dependencies and perform schema decompositions. Notable works include the dependency analysis framework introduced by Bernstein (1976) and the decomposition techniques described by Elmasri and Navathe (2015) .  \nRecent advancements in machine learning have spurred research into its integration with database management tasks. For instance, Agrawal and Srikant (1994) introduced association rule mining, laying the groundwork for data-driven dependency detection. Similarly, Han et al. (2012) highlighted clustering and classification methods applicable to database optimization.  \nIn the realm of automated normalization, researchers like Heidari et al. (2020) explored the use of ML for schema design, focusing on query optimization and indexing. Lu et al. (2021) extended this work by proposing a reinforcement learning approach to automate normaliz","cbCaieGKpx6PFEvT","https://ap.wps.com/l/cbCaieGKpx6PFEvT","pdf",1204401,1,6,"English","en",105,"# Introduction\n# Literature Survey\n# Methodology\n## Input Data\n## Data Preprocessing\n## Functional Dependency Detection\n# Conclusion","[{\"question\":\"Why is data normalization important in supermarket sales databases?\",\"answer\":\"Data normalization organizes relational data to eliminate redundancy and maintain integrity. For large transactional datasets, it also supports optimized query performance and business requirements.\"},{\"question\":\"How does the proposed framework automate data normalization?\",\"answer\":\"The framework combines supervised and unsupervised machine learning to identify functional dependencies, detect anomalies, and suggest optimal schema transformations with reduced human intervention.\"},{\"question\":\"What improvements do experiments show compared with traditional normalization?\",\"answer\":\"Experiments on supermarket sales datasets report substantial gains in accuracy, scalability, and processing time, indicating better performance for large-scale use.\"}]","Utilizing Machine Learning for Automated Data Normalization in Supermarket Sales Databases | PDF",1785936856,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"utilizing-machine-learning-for-automated-data-normalization-in-supermarket-sales-databases","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/utilizing-machine-learning-for-automated-data-normalization-in-supermarket-sales-databases/127102/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is data normalization important in supermarket sales databases?","Question",{"text":76,"@type":77},"Data normalization organizes relational data to eliminate redundancy and maintain integrity. For large transactional datasets, it also supports optimized query performance and business requirements.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed framework automate data normalization?",{"text":81,"@type":77},"The framework combines supervised and unsupervised machine learning to identify functional dependencies, detect anomalies, and suggest optimal schema transformations with reduced human intervention.",{"name":83,"@type":74,"acceptedAnswer":84},"What improvements do experiments show compared with traditional normalization?",{"text":85,"@type":77},"Experiments on supermarket sales datasets report substantial gains in accuracy, scalability, and processing time, indicating better performance for large-scale use.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]