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A perspective on modeling evolution
Author(s) -
Juan Anna,
Mas Sílvia,
Maeder Marcel,
Tauler Romà
Publication year - 2020
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3205
Subject(s) - perspective (graphical) , computer science , focus (optics) , data science , process (computing) , set (abstract data type) , management science , epistemology , artificial intelligence , engineering , philosophy , physics , optics , programming language , operating system
Data modeling is a wide concept that exists since long and encompasses all possible ways to interpret the information associated with a process, analytical measurement or set of related parameters that presents a systematic variation. Data modeling can follow the path of knowledge and be based on first principles or can focus on measurements and empirical models. These different approaches are known as hard‐ and soft‐modeling, respectively. It seemed to us very appropriate to dedicate this article to Paul Gemperline, a person who has significantly contributed to the worlds of hard‐ and soft‐modeling, presumably acknowledging the value of looking at data from all possible perspectives. The following pages do not intend to be an extensive review, but a personal perspective on values, milestones and progress of hard‐ and soft‐modeling and on the necessary existence and valuable combination of both ways to interpret chemical information.