[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118171-en":3,"doc-seo-118171-105":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},118171,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Streaming machine learning algorithms with streaming big data systems","Real-time analytics for continuously arriving information relies on streaming big data systems that process data in motion and support timely decisions. This research investigates how streaming big data systems integrate with machine learning algorithms to extract real-time insights while addressing the evolving behavior of stream data. It reviews streaming system characteristics, then focuses on selecting and adapting algorithms for continuous learning, including preprocessing and feature extraction for real-time streams. The study also examines model training and updating challenges, emphasizing accuracy and efficiency.","Streaming machine learning algorithms with streaming big data  \nsystems  \nAlgoritmos de aprendizado de máquina de transmissão contínua com sistemas de big data de transmissão contínua  \nDOI:10.34117/bjdv10n1-021  \nRecebimento dos originais: 01/12/2023  \nAceitação para publicação: 05/01/2024  \nRamesh Marpu  \nPhD Research Scholar  \nInstitution: BirTikendrajit University  \nAddress: near Manipur University, Canchipur, Kitna Panung, Torban, Manipur 795003,  \nÍndia  \n[E-mail: marpuramesh223@gmail.com](E-mail: marpuramesh223@gmail.com)  \nBairam Manjula  \nPhD Research Supervisor  \nInstitution: BirTikendrajit University  \nAddress: near Manipur University, Canchipur, Kitna Panung, Torban, Manipur 795003,  \nÍndia  \n[E-mail: manjulabairam@kakatiya.ac.in](E-mail: manjulabairam@kakatiya.ac.in)  \nABSTRACT  \nAs the era of big data unfolds, the need for real-time analytics and decision-making becomes increasingly crucial. Streaming big data systems, designed to process and analyse data in motion, have emerged as a pivotal solution for handling vast streams of continuously arriving information. This research delves into the synergy between streaming big data systems and machine learning algorithms, aiming to harness the power of real-time insights. We explore the challenges and opportunities presented by the dynamic nature of streaming data, emphasizing the importance of adapting traditional machine learning methodologies to suit the evolving requirements of streaming environments.The research begins with an overview of streaming big data systems, laying the foundation for understanding the unique characteristics of data in motion. We then delve into the selection and adaptation of machine learning algorithms that are well-suited for continuous learning and updating. Key aspects of the research include the preprocessing and feature extraction techniques tailored for real-time data streams, ensuring the effective utilization of streaming machine learning algorithms. The paper provides insights into the challenges of model training and updating in a dynamic environment, emphasizing the importance of accuracy and efficiency.  \nKeywords: streaming big data, machine learning algorithms, decision-making,streaming SVM.  \nRESUMO  \nÀ medida que a era dos grandes dados se desenrola, a necessidade de análises e tomadade decisões em tempo real torna-se cada vez mais crucial. Os sistemas de transmissão contínua de grandes volumes de dados, concebidos para processar e analisar dados em  \nmovimento, surgiram como uma solução essencial para lidar com vastos fluxos deinformações que chegam continuamente. Esta pesquisa aprofunda a sinergia entre sistemas de streaming de big data e algoritmos de aprendizagem de máquina, com o objetivo de aproveitar o poder de insights em tempo real. Exploramos os desafios e asoportunidades apresentadas pela natureza dinâmica dos dados de streaming, enfatizando a importância de adaptar metodologias tradicionais de aprendizagem de máquina para atender aos requisitos em evolução dos ambientes de streaming.A pesquisa começa com uma visão geral dos sistemas de streaming de big data, estabelecendo a base para acompreensão das características únicas dos dados em movimento. Então, nosaprofundamos na seleção e adaptação de algoritmos de aprendizagem de máquina que são adequados para aprendizagem contínua e atualização. Os principais aspectos da pesquisaincluem as técnicas de pré-processamento e extração de recursos adaptadas para fluxos de dados em tempo real, garantindo a utilização eficaz de algoritmos de aprendizagem demáquina de streaming. O artigo fornece insights sobre os desafios do treinamento e daatualização de modelos em um ambiente dinâmico, enfatizando a importância da precisãoe da eficiência.  \nPalavras-chave: streaming de big data, algoritmos de aprendizado de máquina, tomadade decisão, streaming SVM.  \n1 INTRODUCTION  \nStreaming big data systems operate in an environment characterized by the continuous flow of data. Theoret","cbCaisiwga0v84Wh","https://ap.wps.com/l/cbCaisiwga0v84Wh","pdf",506797,1,18,"English","en",105,"# Abstract\n## Streaming big data systems overview\n## Algorithm selection and continuous learning\n## Preprocessing and feature extraction for real-time streams\n## Model training and updating challenges","[{\"question\":\"What problem do streaming big data systems address for analytics and decision-making?\",\"answer\":\"They process and analyze data in motion from continuously arriving streams, enabling real-time analytics and timely decisions despite the high volume and speed of incoming information.\"},{\"question\":\"How does the research connect streaming big data systems with machine learning algorithms?\",\"answer\":\"It explores the synergy between streaming big data systems and machine learning algorithms to leverage real-time insights, while adapting traditional learning methods to fit streaming environments.\"},{\"question\":\"Which techniques are highlighted for effective use of streaming machine learning algorithms?\",\"answer\":\"The study emphasizes preprocessing and feature extraction techniques tailored for real-time data streams to ensure the algorithms can utilize streaming data effectively.\"}]","Streaming machine learning algorithms with streaming big data systems | 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problem do streaming big data systems address for analytics and decision-making?","Question",{"text":75,"@type":76},"They process and analyze data in motion from continuously arriving streams, enabling real-time analytics and timely decisions despite the high volume and speed of incoming information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the research connect streaming big data systems with machine learning algorithms?",{"text":80,"@type":76},"It explores the synergy between streaming big data systems and machine learning algorithms to leverage real-time insights, while adapting traditional learning methods to fit streaming environments.",{"name":82,"@type":73,"acceptedAnswer":83},"Which techniques are highlighted for effective use of streaming machine learning algorithms?",{"text":84,"@type":76},"The study emphasizes preprocessing and feature extraction techniques tailored for real-time data streams to ensure the algorithms can utilize streaming data 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