Oct 15, 2014 04:18 PM
http://www.eurekalert.org/pub_releases/2...101414.php
Big Data analytics are helping to provide answers to many complex problems in science and society, but they have not contributed to a better understanding climate science, despite an abundance of climate data. When it comes to analyzing the climate system, Big Data methods alone are not enough and sound scientific theory must guide data modeling techniques and results interpretation, according to an insightful article in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the Big Data website.
In "A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science," James Faghmous, PhD and Vipin Kumar, PhD, The University of Minnesota--Twin Cities, explore the challenges and opportunities for mining large climate datasets and the subtle differences that are needed compared to traditional Big Data methods if accurate conclusions are to be drawn. The authors discuss the importance of combining scientific theory and First Principles with Big Data analytics and use examples from existing research to illustrate their novel approach.
"This paper is a great example of leveraging the abundance of climate data with powerful analytical methods, scientific theory, and solid data engineering to explain and predict important climate change phenomena," says Big Data Editor-in-Chief Vasant Dhar, Co-Director, Center for Business Analytics, Stern School of Business, New York University.
Big Data analytics are helping to provide answers to many complex problems in science and society, but they have not contributed to a better understanding climate science, despite an abundance of climate data. When it comes to analyzing the climate system, Big Data methods alone are not enough and sound scientific theory must guide data modeling techniques and results interpretation, according to an insightful article in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the Big Data website.
In "A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science," James Faghmous, PhD and Vipin Kumar, PhD, The University of Minnesota--Twin Cities, explore the challenges and opportunities for mining large climate datasets and the subtle differences that are needed compared to traditional Big Data methods if accurate conclusions are to be drawn. The authors discuss the importance of combining scientific theory and First Principles with Big Data analytics and use examples from existing research to illustrate their novel approach.
"This paper is a great example of leveraging the abundance of climate data with powerful analytical methods, scientific theory, and solid data engineering to explain and predict important climate change phenomena," says Big Data Editor-in-Chief Vasant Dhar, Co-Director, Center for Business Analytics, Stern School of Business, New York University.