Theory-guided data science for climate change

Computer

Volumn 47, Issue 11, 2014, Pages 74-78

  Faghmous, James H ^a   Banerjee, Arindam ^a   Shekhar, Shashi ^a   Steinbach, Michael ^a   Kumar, Vipin ^a   Ganguly, Auroop R ^b   Samatova, Nagiza ^c  

^a UNIVERSITY OF MINNESOTA   (United States)

^b NORTHEASTERN UNIVERSITY   (United States)

^c NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

big data; climate change; data analysis; data mining; discovery analytics; scientific computing; theory guided data science

Indexed keywords

BIG DATA; COMPUTATION THEORY; DATA MINING; DATA REDUCTION; NATURAL SCIENCES COMPUTING; PHYSICAL ADDRESSES;

DISCOVERY ANALYTICS; PHYSICAL NATURE; SCIENCE METHODS; THEORY-GUIDED DATA SCIENCE;

CLIMATE CHANGE;

EID: 84913549532     PISSN: 00189162     EISSN: None     Source Type: Trade Journal    
DOI: 10.1109/MC.2014.335     Document Type: Article

References (4)

1. J.H. Faghmous and V. Kumar, "A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science," Big Data, vol. 2, no. 3, 2014; doi:10.1089/big.2014.0026.
2. P.M. Caldwell et al., "Statistical Significance of Climate Sensitivity Predictors Obtained by Data Mining," Geophysical Research Letters, vol. 41, no. 5, 2014, pp. 1803-1808.
3. J.H. Faghmous et al., "Spatiotemporal Consistency as a Means to Identify Unlabeled Objects in a Continuous Data Field," Proc. 28th AAAI Conf. Artificial Intelligence (AAAI 14), 2014, pp. 410-416.
4. S. Chatterjee et al., "Sparse Group Lasso: Consistency and Climate Applications," Proc. 12th SIAM Int'l Conf. Data Mining (SDM 12), 2012, pp. 47-58.