Open AccessIdentifying the Dimensions of Data Science as a Space
Author(s) -
Rongfu Huang,
Xinlin Cai,
Yuhang Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1616/1/012043
Subject(s) - big data , speculation , data science , perspective (graphical) , computer science , space (punctuation) , object (grammar) , value (mathematics) , space science , data processing , data mining , engineering , database , artificial intelligence , machine learning , economics , macroeconomics , operating system , aerospace engineering
An increasing number of data with great potential value are produced continuously, which exceeds the capacity of the existing computing system and promotes the generation of new basic theories about big data and its processing. Such problems as the relationship between big data and small data and that between big data and the existing scientific system need to be accurately solved by the academic circle through the establishment of data science. However, data science is still in its infancy and the basic dimensions of data science need to be identified. In this paper, five categories demanding for establishment of data science are summarized. A new way to understand big data that gives the most important speculation and research object of data science from a global perspective is developed.