메뉴 건너뛰기




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

Theory-guided data science for climate change

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;

EID: 84913549532     PISSN: 00189162     EISSN: None     Source Type: Trade Journal    
DOI: 10.1109/MC.2014.335     Document Type: Article
Times cited : (29)

References (4)
  • 1
    • 84991818059 scopus 로고    scopus 로고
    • A big data guide to understanding climate change: The case for theory-guided data science
    • 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.
    • (2014) Big Data , vol.2 , Issue.3
    • Faghmous, J.H.1    Kumar, V.2
  • 2
    • 84895643490 scopus 로고    scopus 로고
    • Statistical significance of climate sensitivity predictors obtained by data mining
    • 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.
    • (2014) Geophysical Research Letters , vol.41 , Issue.5 , pp. 1803-1808
    • Caldwell, P.M.1
  • 3
    • 84908206235 scopus 로고    scopus 로고
    • Spatiotemporal consistency as a means to identify unlabeled objects in a continuous data field
    • 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.
    • (2014) Proc. 28th AAAI Conf. Artificial Intelligence (AAAI 14) , pp. 410-416
    • Faghmous, J.H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.