[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121139-en":3,"doc-seo-121139-105":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},121139,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Integration of Machine Learning Algorithms with Cloud Computing for Real-Time Data Analysis - Paper","Study examines real-time data analysis by combining machine learning algorithms with cloud computing. The work frames optimization problems and development directions while emphasizing improved efficiency in real-time processing compared with traditional systems that often incur high maintenance costs and higher failure risks. Key benefits include enhanced indexing, structured information storage and retrieval, and query optimization, supporting operational proficiency and better decision-making despite constraints such as limited resources and data privacy challenges. Trends such as Explainable AI, Automated ML, and continuous intelligence are highlighted as enablers.","Integration of Machine Learning Algorithms with Cloud Computing for  \nReal-Time Data Analysis  \nDevidas Kanchetti1, Rajesh Munirathnam2 and Darshit Thakkar3  \n1Independent Researcher, USA.  \n2Independent Researcher, USA.  \n3Independent Researcher, USA.  \n[www.jrasb.com || Vol. 2 No. 5](www.jrasb.com || Vol. 2 No. 5) (2023): October Issue  \nReceived: 21-04-2024 Revised: 26-04-2024 Accepted: 20-05-2024  \nABSTRACT  \nAs part of this study though, real-time data analysis is examined by exploring the combination of machine learning algorithms with cloud computing. It does so by defining optimization problems and solutions, as well as outlining optimization goals and directions for development. The efficiency of real time data processing ability is also amplified with the use of an amalgamation of machine learning and cloud computing though traditional systems are often associated with high failure ratesand high costs of maintenance. Enhanced indexing, systematic control of information storage and retrieval, query optimization are the main benefits obtainable from this. This is because despite the challenges such as limited resources, integration has never been a problem even with challenges of data privacy. Peculiar trends such as Explainable AI, Automated ML, and Continuous Intelligence present the ability to substantially enhance operational proficiency and decision-making.  \nKeywords-Real-time data analysis, Machine learning algorithms, Cloud computing, Optimization problems, Optimization goals, Real-time data processing, Enhanced indexing.  \nI. INTRODUCTION  \nThe use of data has become critical for any business as significant factors for strategic management, productivity, and decision-making. Alleviating this issue requires data warehouses that centralize information gathering within an organization to manage this tidal wave of data. Data warehouses have been relatively significant to facilitate data handling and analysis. To ensure that these systems remain functional they need tobe normalized because of the increasing volume and the nature of the data that may be more complex. Cloud computing and master learning are employed in this upgrade. ‘Machine learning’ is another branch of artificial intelligence that enables systems to learn, improving overtime the performance of the particular task. For the purpose of the given article, within the paradigm of data warehousing, machine learning algorithms have the potential of tweaking several activities, such as data handling, indexing, and query processing, all of which can help decrease the latency and enhance the given system`s  \nthroughput. It is advantageous to incorporate machine learning into data warehousing to aid resources with adaptive resource allocation, automatic query optimization, as well as to introduce predictable supply chain analytics for workload management. As opposed, cloud computing is an elastic solution that enables the processing of computations of ML algorithms on scalable and flexible infrastructure. This allows organizations to perform complex data processing in real time whilst at the same time avoiding the high capital investment in raw computing resources and analytical tools by leveraging cloud resources. Also, the processing, storing, and integration of data within the cloud are easy tasks which make the cloud an ideal environment for the implementation of data warehousing with improved features from ML technology.  \nII. LITERATURE REVIEW  \nAccording to Li etal 2024: The evolving nature of data warehousing and data management, pointing out  \nthe shortcomings of the centralized data warehouse approach, which such tools as SAP BO and IBM Cognos exhibit when dealing with the vast amount of data generated by online environments. Thus, in order to tackle these issues, it introduces the concept of cloud data warehousing and Machine Learning. By enabling other recommendations in the data processing, analytics, and storage, this integration helps to enhanc","cbCaimwIkt6LCwD2","https://ap.wps.com/l/cbCaimwIkt6LCwD2","pdf",158343,1,6,"English","en",105,"# Introduction\n# Literature Review\n# Machine Learning and Cloud-Based Optimization (Overview)\n# Real-Time Data Processing Efficiency and Indexing\n# Explainable AI and Automated ML Trends\n# Benefits, Challenges, and Future Directions","[{\"question\":\"How does the document connect machine learning with cloud computing for real-time data analysis?\",\"answer\":\"It explains that machine learning helps optimize data handling tasks (indexing and query processing) while cloud computing provides elastic infrastructure to run ML computations on scalable resources for real-time processing.\"},{\"question\":\"What optimization goals and problems are discussed?\",\"answer\":\"The document frames the study around defining optimization problems and solutions, outlining optimization goals and development directions that improve system performance, reduce latency, and increase throughput.\"},{\"question\":\"What advantages are expected from the integration?\",\"answer\":\"The text highlights enhanced indexing, systematic control of storage and retrieval, and query optimization, which together improve operational efficiency and support stronger decision-making.\"}]","Integration of Machine Learning Algorithms with Cloud Computing for Real-Time Data Analysis - 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