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Gelman, A., Hill, J., 2007. Data Analysis Using Regression and Multilevel/Hierarchical
Models. Cambridge University Press, New York, NY.
Ghemawat, S., Gobioff, H., Leung, S.-T., 2003. In: The Google file system.Proceedings of
SOSP’03 the Nineteenth ACM Symposium on Operating Systems Principles, Bolton
Landing, New York, USA, Oct. 19–22, 2003pp. 29–43.
Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep Learning. MIT Press.http://www.
deeplearningbook.org.
Gupta, S., Nikolaou, M., Saputelli, L., Panjwani, S., 2015. In: Applying Predictive Analytics
to Detect and Diagnose Impending Problems in Electric Submersible Pumps Used
for Lifting Oil From Wellbores.Paper 419054, Presented at AIChE Annual Meeting, Salt
Lake City, UT, USA, Nov, 8–13. https://aiche.confex.com/aiche/2015/webprogram/
ataglance.html.
Hallac, D., Leskovec, J., Boyd, S., 2015. In: Network Lasso: Clustering and Optimization in
Large Graphs.Presented at KDD’15, Sydney, NSW, Aug. 10–13, 2015. http://www.
kdd.org/kdd2015/.
Handy, A., 2015. Arun Murthy Discusses the Future of Hadoop. SD Times.http://sdtimes.
com/arun-murthy-discusses-the-future-of-hadoop/.
Hastie, T., Tibshirani, R., Friedman, J., 2011. The Elements of Statistical Learning: Data
Mining, Inference and Prediction. Springer, NY.
Holsman, R., Richards, B., 2016. The 2016 Upstream Oil and Gas Digital Trends urvey,
Transcript by Accenture, 16-0274. https://www.accenture.com/us-en/insight-2016-
upstream-oil-gas-digital-trends-survey.
Holdaway, K.R., 2014. Harness Oil & Gas Big Data With Analytics: Optimize Exploration
and Production with Data-Driven Models. Wiley, Hoboken, NJ. http://www.wiley.
com/WileyCDA/WileyTitle/productCd-1118779312.html.
James, G., Witten, D., Hastie, T., Tibshirani, R., 2014. An Introduction to Statistical Learn-
ing With Applications in R. Springer, NY.
Jordan, M.I., Mitchell, T.M., 2015. Machine learning: trends, perspectives, and prospects.
Science 349 (6245), 255–260.
Kale, D.Z.,David, A.,Heuermann-Kuehn,L.,Fanini, O.,2015.Methodology forOptimizing
Operational Performance and Life Management of Drilling Systems Using Real Time-
Data and Predictive Analytics. SPE-173419-MS. https://doi.org/10.2118/173419-MS.
Laney, D., 2012. In: Information, Economics, Big Data and the Art of the Possible with
Analytics.Presentation by Gartner Inc. https://www-01.ibm.com/events/wwe/
grp/grp037.nsf/vLookupPDFs/Gartner_Doug-%20Analytics/$file/Gartner_Doug-%
20Analytics.pdf.
Leskovec, J., Rajaraman, A., Ullman, J.D., 2014. Mining of Massive Datasets, second ed.
Cambridge University Press, Cambridge, The United Kingdom.
Lochmann, M., Brown, I., 2016. Intelligent Energy: A Strategic Inflection Point. SPE
170630-PA. https://doi.org/10.2118/170630-PA.
Markets and Markets, 2015. Digital Oil Field Market—Global Forecast to 2020. Report
Code EP2720.
Maucec, M., Bhattacharya, S., Yarus, J.M., Fulton, D.D., Singh, A.P., 2012. System,
Method and Computer Program Product for Multivariate Statistical Validation of Well
Treatment and Stimulation Data, PCT Patent Application 2012-IP-061475.
Maucec, M., Singh, A.P., Bhattacharya, S., Yarus, J., Fulton, D., Orth, J., 2013. Multivariate
Analysis of Job Pause Time Data Using Classification and Regression Tree and Kernel
Clustering. SPE-167399-MS. https://doi.org/10.2118/167399-MS.
Maucec, M., Singh, A.P., Bhattacharya, S., Yarus, J., Fulton, D., Orth, J., 2015. Multivariate
Analysis and Data Mining of Well Stimulation Data Using Classification and Regression
Tree With Enhanced Interpretation and Prediction Capabilities. SPE-166472-PA.
https://doi.org/10.2118/166472-PA.