{"title":"Data-Intensive Science: Problems and Development of the Fourth Paradigm","authors":"A. O. Erkimbaev, V. Yu. Zitserman, G. A. Kobzev","doi":"10.3103/s0005105524700109","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The article examines the evolution and current state of the data intensive sciences (DISs). The article focuses on approaches to methods of data mining generated by the development of artificial intelligence. It is noted that the rich opportunities of new approaches have caused unreasonable enthusiasm among scientists with respect to their capabilities, while the achieved level of knowledge is clearly ignored. It is shown how numerous facts of limited data processing potential have gradually accumulated without taking into account all previously established laws of nature and research methods. A significant role in the awareness of the real potential of working with data (including big data methods) was played by specialists in the field of methodology of science, who created a new direction, the epistemology of the DIS. Various ways and means of introducing expert knowledge at subsequent stages of analysis in the form of machine learning are listed. In sum, the appearance is noted of special algorithms for physically informed machine learning using data in combination with a traditional approach based on solving equations of mathematical physics.</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s0005105524700109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The article examines the evolution and current state of the data intensive sciences (DISs). The article focuses on approaches to methods of data mining generated by the development of artificial intelligence. It is noted that the rich opportunities of new approaches have caused unreasonable enthusiasm among scientists with respect to their capabilities, while the achieved level of knowledge is clearly ignored. It is shown how numerous facts of limited data processing potential have gradually accumulated without taking into account all previously established laws of nature and research methods. A significant role in the awareness of the real potential of working with data (including big data methods) was played by specialists in the field of methodology of science, who created a new direction, the epistemology of the DIS. Various ways and means of introducing expert knowledge at subsequent stages of analysis in the form of machine learning are listed. In sum, the appearance is noted of special algorithms for physically informed machine learning using data in combination with a traditional approach based on solving equations of mathematical physics.
期刊介绍：
Automatic Documentation and Mathematical Linguistics is an international peer reviewed journal that covers all aspects of automation of information processes and systems, as well as algorithms and methods for automatic language analysis. Emphasis is on the practical applications of new technologies and techniques for information analysis and processing.
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