INF 610 Big Data and Analytics: Reading list

Module texts

• There is no textbook. 

Recommended readings

• Acerbi, A., Lampos, V., Garnett, P. and Bentley, R.A. (2013). The expression of emotions in 20th century books. PLoS ONE, vol. 8(3), e59030.

• Ahn, Y., Ahnert, S.E., Bagrow, J. P. and Barabási, A. (2011). Flavor network and the principles of food pairing. Scientific Reports 1, Article number: 196. Open resource

• Anderson, C. (2008). The end of theory: the data deluge makes the scientific method obsolete. Wired magazine.

• Cattell, R. (2010). Scalable SQL and NoSQL Data Stores. SIGMOD Record, vol. 39 (4). Open resource

• 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. Request item

• Dean, J. and Ghemawat, S. (2010). MapReduce: a flexible data processing tool. Communications of the ACM, vol. 53(1), pp. 72-77.

• Domingos, P. (2012).  A few useful things to know about machine learning. CACM, vol. 55(10).

• Heer, J., Bostock, M. and Ogievetsky, D. (2010). A tour through the visualization zoo. Communications of the ACM, vol. 53(6).

• 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, vol.2(1), p.24.

• Lazer, D., Kennedy, R., King, G. and Vespignani, A. (2014). The parable of Google flu trends: traps in big data analysis. Science, vol. 343(6176), pp. 1203-1205. Request item

• L'Heureux, A., Grolinger, K., ElYamany, H. F. and Capretz, M. (2017). Machine learning with big data: challenges and approaches. IEEE Access. Open access

• 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, vol. 234, pp.11-26.

• Lohr, S. (2014). Google flu trends: the limits of big data. NYTimes, March 28. Open access

• 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, vol.2(1), p.1.

• 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. Open access

• Sapountzi, A. and Psannis, K.E. (2016). Social networking data analysis tools & challenges. Future Generation Computer Systems.

• Stonebraker, M. (2010). MapReduce and parallel DBMS’s: friends or foes? Communications of the ACM, vol. 53(1), pp. 64-71. Open access

• Wu, X. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, vol. 14.

• 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/

• Hans Rosling, The Joy of Stats

• Pat Hanaran, Tools for Data Enthusiasts