[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117399-en":3,"doc-seo-117399-105":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},117399,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning in Business Intelligence","Machine Learning in Business Intelligence research analyzes the state of the art, scopes, and challenges of BI and ML, then identifies knowledge gaps where ML can solve business problems optimally. The work proposes an adaptive business intelligence conceptual framework by combining ML techniques, algorithms, technologies, and the central Business Intelligence Machine (BIM). A systematic review traces chronological evolution of BI/ML terms, technologies, and analytics, and argues the ML engine enables faster, more accurate, and more automated decision making with minimal human involvement.","Machine Learning in Business Intelligence  \nMr. Kopelo Letou1*, Dr. S.Thiyagarajan2,  \n1*Computer Science and Engineering, St. Joseph University, Chümoukedima,  \nNagaland,[India. E-mail:kopsystem@gmail.com](India. E-mail:kopsystem@gmail.com)  \n2Computer Science and Engineering, St.Joseph University, Chümoukedima,  \nNagaland,[India. E-mail:tyagarajin@yahoo.com](India. E-mail:tyagarajin@yahoo.com)  \nAbstract:-The purposed of this research have the fivefold: First We did in-depth deliberate studies on Business Intelligence (BI) and Machine Learning (ML) technology to provide overview of the state-of-art of its scopes and challenges. Second, to understand and identify knowledge gaps BI that the problems of business can be solve in optimal through the techniques and algorithms of ML. Third, we have developed conceptual framework of adaptive business intelligence model to solve the problems of businesses by incorporating a characteristics, techniques, algorithms and technology of machine learning. The key concept was to developed Business Intelligence Machine (BIM) which was the whole system of adaptive business intelligence models in-order to automate business processes, intelligent decision making and obtain optimal business desire goal. Fourth, a systematic review was done on Business Intelligence and Machine Learning along with their chronological and evolution of terminologies, technologies, techniques, algorithms, classifications, analytics, capabilities and Business Intelligence Machine applications. Fifth the paper proposed adaptive business intelligence model which was the solution to existing business intelligence. Machine learning engine is the heart and soul component of adaptive business intelligence model. Business intelligence machine can solve real world business problems with minimal human involvement, more accurate, more speed, quick response and better decision making in business as compare to existing of business intelligence systems functionalities. In this research paper, we have open look_forward ways to future research directions on the related areas of Business Intelligence from non_adaptive model to adaptive BI model using machine learning technology to solve real world business problems. Different machine learning techniques and algorithms can overcome the existing challenges of business intelligence systems.  \nKeywords:-Business, Intelligence, Business Intelligence (BI), Machine Learning (ML), Adaptive BI Model, ML Engine, Business Intelligence Machine (BIM)  \nI. Introduction  \nIn this modern era rapid growing of artificial intelligence (AI), machine learning (ML) technology is the solutions of business problems face in this real world. Business is part and partial of our daily life may it be individual or company or industry or firm either we like it or not, it will co-exist with the existing of life. ML is the heart and soul of AI component. Artificial intelligence is software to mimic aspects of human intelligence including perceiving, planning, reasoning, learning, predicting, et cetera. The novelty of this paper was that ML revolutionized andreshaping various aspects of the old decades terminologies of business intelligence and machine learning. Machine learning technology involve in business intelligence is a new way to solve the current problems of businesses. The problems of businesses are solves by adopting from nonadaptive or primitive or conventional methods of business intelligence to adaptive business intelligence model using machine learning techniques and algorithms. There is in need of machine learning technology in business intelligence to solve real world business problems, quick  \nbusiness decision making, better service to firms and customers, automate resolve operational business problems, quick automate response to market trends without delay, more accurate estimation, better prediction on business’s operational activities and to obtain maximum profits from businesses. In this first ","cbCaipERbZ6n8wRI","https://ap.wps.com/l/cbCaipERbZ6n8wRI","pdf",506755,1,15,"English","en",105,"# Abstract\n# Introduction\n## Chronicle and Evolution of Business Intelligence\n## Chronicle and Evolution of Machine Learning","[{\"question\":\"What is the main goal of this research on business intelligence?\",\"answer\":\"It aims to study BI and ML in depth, identify knowledge gaps, and propose an adaptive BI framework that uses machine learning to address real business problems.\"},{\"question\":\"How does the adaptive business intelligence model work according to the paper?\",\"answer\":\"It incorporates ML characteristics, techniques, algorithms, and technology, centered on the Business Intelligence Machine (BIM) to automate business processes and support intelligent decision making.\"},{\"question\":\"What does the “ML engine” contribute to the proposed model?\",\"answer\":\"The paper describes the ML engine as the core component that enables real-world problem solving with minimal human involvement, improving speed, accuracy, response time, and decision quality.\"}]","Machine Learning in Business Intelligence | 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