[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118019-en":3,"doc-seo-118019-105":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},118019,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Dynamic Data Scaling Techniques for Streaming Machine Learning","Innovative dynamic data scaling techniques are investigated for streaming machine learning environments where real-time data evolves in distribution and pattern over time. Static scaling approaches often struggle to remain effective under changing stream characteristics, motivating adaptive methods that update scaling parameters as new data arrives. The research evaluates and compares dynamic scaling against static baselines to improve predictive accuracy and preserve model relevance in fast, dynamic streaming contexts. Findings support scalable and responsive machine learning for timely insights.","Dynamic Data Scaling Techniques for Streaming Machine Learning  \n(IJGASR) International Journal For Global Academic & Scientific Research ISSN Number: 2583-3081  \nVolume 3, Issue No. 1, 1–12 © The Author 2024  \n[journals.icapsr.com/index.php/ijgasr](journals.icapsr.com/index.php/ijgasr)[ ](journals.icapsr.com/index.php/ijgasr)DOI: 10.55938/ijgasr.v3i1.68  \nPriyanka Kaushik  \nAbstract  \nThis research delves into innovative dynamic data scaling techniques designed for streaming machine learning environments. In the realm of real-time data streams, conventional static scaling methods may encounter challenges in adapting to evolving data distributions. To overcome this hurdle, our study explores dynamic scaling approaches capable of adjusting and optimizing scaling parameters dynamically as the characteristics of incoming data shift over time. The objective is to augment the performance and adaptability of machine learning models in streaming scenarios by ensuring that the scaling process remains responsive to changing patterns in the data. Through empirical evaluations and comparative analyses, the study aims to showcase the efficacy of the proposed dynamic data scaling techniques in enhancing predictive accuracy and sustaining model relevance in dynamic and fast-paced streaming environments. This research contributes to the advancement of scalable and adaptive machine learning methodologies, particularly in applications where timely and accurate insights from streaming data are crucial.  \nKeywords  \nAdaptive Scaling Methods, Changing Data Patterns, Scaling Parameters, Dynamic Data Scaling, Predictive Accuracy  \nIntroduction  \nIn the landscape of contemporary data analytics, the integration of machine learning with real-time streaming data has become pivotal for applications such as online forecasting, fraud detection, and dynamic decision-making. However, the efficacy of machine learning models in these environments is heavily contingent upon their ability to adapt to the evolving nature of streaming data. Traditional data scaling techniques, while effective in batch processing, often prove inadequate for the dynamic and  \nProfessor, AIT-CSE, Chandigarh University.  \nCorresponding Author:  \nDr. Priyanka Kaushik Professor, AIT-CSE, Chandigarh University.  \nE-mail: [kaushik.priyanka17@gmail.com](kaushik.priyanka17@gmail.com)  \n© 2024 by Dr. Priyanka Kaushik Submitted for possible open access publication under the terms and conditions  \nof the Creative Commons Attribution (CC BY) license,([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). This work is licensed under a Creative Commons Attribution 4.0 International License  \nfast-paced characteristics inherent in streaming scenarios. To address this challenge, this study focuses on dynamic data scaling techniques specifically tailored for streaming machine learning. The objective is to enhance the adaptability and performance of machine learning models by developing scaling methodologies that can dynamically adjust to the changing data distributions and patterns encountered in real-time streaming environments. Through an exploration of innovative approaches and empirical validations, this research aims to contribute to the advancement of scalable and responsive machine learning methodologies, offering insights into the optimization of model accuracy and relevance in dynamic streaming contexts. In the landscape of contemporary data analytics, the integration of machine learning with real-time streaming data has become pivotal for applications such as online forecasting, fraud detection, and dynamic decision-making. However, the efficacy of machine learning models in these environments is heavily contingent upon their ability to adapt to the evolving nature of streaming data. Traditional data scaling techniques, while effective in batch processing, often prove inadequate for the dynamic and fast-paced characteristics inherent in streaming scen","cbCaibze4fwYIZIB","https://ap.wps.com/l/cbCaibze4fwYIZIB","pdf",244346,1,12,"English","en",105,"# Introduction\n## Objectives\n## Methods and Evaluation\n## Results and Discussion","[{\"question\":\"Why do traditional static scaling methods underperform in streaming machine learning?\",\"answer\":\"Static scaling is often inadequate when incoming data distributions shift over time, which is common in real-time streaming scenarios.\"},{\"question\":\"What is the core idea behind dynamic data scaling techniques?\",\"answer\":\"The techniques dynamically adjust and optimize scaling parameters to match changing characteristics of incoming data streams.\"},{\"question\":\"How is the effectiveness of dynamic scaling assessed in the study?\",\"answer\":\"Through empirical evaluations and comparative analyses against models using traditional static scaling, focusing on predictive accuracy and sustained relevance.\"}]","Dynamic Data Scaling Techniques for Streaming Machine Learning | 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