AI 508 Big Data Technologies and Applications: Reading list

There is no textbook. We will rely on web resources and papers, including:

• L'Heureux, A., Grolinger, K., ElYamany, H. F., & Capretz, M. (2017). Machine Learning with Big Data: Challenges and Approaches. IEEE Access.
• Dave, M. and Gianey, H. (2016). Different clustering algorithms for Big Data analytics: A review. In Proceedings of the International Conference on System Modeling & Advancement in Research Trends (SMART), (pp. 328-333). IEEE. 
• Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R. and Muharemagic, E., 2015. Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), p.1.
• Landset, S., Khoshgoftaar, T.M., Richter, A.N. and Hasanin, T., 2015. A survey of open source tools for machine learning with big data in the Hadoop ecosystem. Journal of Big Data, 2(1), p.24.
• Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y. and Alsaadi, F.E., 2017. A survey of deep neural network architectures and their applications. Neurocomputing, 234, pp.11-26.
• Sapountzi, A. and Psannis, K.E., 2016. Social networking data analysis tools & challenges. Future Generation Computer Systems.
• Salloum, S., Dautov, R., Chen, X., Peng, P.X. and Huang, J.Z., 2016. Big data analytics on Apache Spark. International Journal of Data Science and Analytics, pp.1-20. 
• Yong-Yeol Ahn, Sebastian E. Ahnert, James P. Bagrow, Albert-László Barabási (2011)
• Flavor network and the principles of food pairing, Scientific Reports 1, Article number: 196 doi:10.1038/srep00196
• Acerbi A, Lampos V, Garnett P, Bentley RA (2013) The Expression of Emotions in 20th Century Books. PLoS ONE 8(3): e59030. doi:10.1371/journal.pone.0059030 
• David Lazer, Ryan Kennedy, Gary King, Alessandro Vespignani, The Parable of Google Flu Trends: Traps in Big Data Analysis, Science 14 March 2014: Vol. 343 no. 6176 pp. 1203-1205 (paywalled)
• Steve Lohr, Google Flu Trends: The Limits of Big Data, NYTimes, March 28, 2014
• Chris Anderson,  “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete” , Wired magazine, 2008 
• Stonebraker et al., “MapReduce and Parallel DBMS’s: Friends or Foes?”, Communications of the ACM, January 2010. 
• Dean and Ghemawat, “MapReduce: A Flexible Data Processing Tool”, Communications of the ACM, January 2010.
• Rick Cattell, “Scalable SQL and NoSQL Data Stores”, SIGMOD Record, December 2010 (39:4) 
• Benford's Law (wikipedia)
• Frequentist vs. Bayesian Perspectives explained, Jake Vanderplas, UW eScience Institute
• http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/
• http://jakevdp.github.io/blog/2014/06/06/frequentism-and-bayesianism-2-when-results-differ/
• http://jakevdp.github.io/blog/2014/06/12/frequentism-and-bayesianism-3-confidence-credibility/
• http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/
• Xindong Wu et al., Top 10 Algorithms in Data Mining, Knowledge and Information Systems, 14(2008), 1: 1-37.  (read section on C4.5)
• Pedro Domingos, A Few Useful Things to Know about Machine Learning, CACM 55(10), 2012
• Xindong Wu et al., Top 10 Algorithms in Data Mining, Knowledge and Information Systems, 14(2008), 1: 1-37.  (read section on k-means)
• Jeffrey Heer, Michael Bostock, Vadim Ogievetsky, A Tour through the Visualization Zoo, Communications of the ACM, Volume 53 Issue 6, June 2010
• Hans Rosling, The Joy of Stats  n
• Pat Hanaran, Tools for Data Enthusiasts