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Book and article examples on Big Data General and mechanical engineering: Bishop, Christopher M. Pattern Recognition and Machine Learning. Information Science and Statistics. New York: Springer, 2006. Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. Adaptive Computation and Machine Learning Series. Cambridge, MA: MIT Press, 2012. VanderPlas, Jake. Python Data Science Handbook: Essential Tools for Working with Data. First edition. Beijing Boston Farnham: O’Reilly, 2016. BigData and auditing: Alles, M., & Gray, G. L. (2016). Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors. International Journal of Accounting Information Systems, 22, 44-59. BigData and Management and Networked Business MIS Quarterly (Management Information Systems Quarterly) VOL 40,4, 2016 Special Issue. Transformational Issues of Big Data and Analytics in Networked Business Transformational Issues of Big Data and Analytics in Networked Business Bart Baesens, Ravi Bapna, James R. Marsden, Jan Vanthienen, and J. Leon Zhao . . . . . . . . . . . . . . . . . . 807 A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data Inbal Yahav, Galit Shmueli, and Deepa Mani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 Large-Scale Network Analysis for Online Social Brand Advertising Kunpeng Zhang, Siddhartha Bhattacharyya, and Sudha Ram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849 Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics David Martens, Foster Provost, Jessica Clark, and Enric Junqué de Fortuny . . . . . . . . . . . . . . . . . . . . . . . . 869 Toward a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior Anindya Ghose and Vilma Todri-Adamopoulos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 889 Using Big Data to Model Time-Varying Effects for Marketing Resource (Re)Allocation Alok R. Saboo, V. Kumar, and Insu Park . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 911 Crowd-Squared: Amplifying the Predictive Power of Search Trend Data Erik Brynjolfsson, Tomer Geva, and Shachar Reichman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 941 Privacy and Big Data: Scalable Approaches to Sanitize Large Transactional Databases for Sharing Syam Menon and Sumit Sarkar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 963 Mobile App Analytics: A Multiple Discrete-Continuous Choice Framework Sang Pil Han, Sungho Park, and Wonseok Oh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 983 Comprehensible Predictive Models for Business Processes Dominic Breuker, Martin Matzner, Patrick Delfmann, and Jörg Becker . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1009 Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence Zhan (Michael) Shi, Gene Moo Lee, and Andrew B. Whinston . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035 Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics Wolfgang Ketter, Markus Peters, John Collins, and Alok Gupta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1057 BigData and analytics Linnet Taylor, Ralph Schroeder and Eric Meyer (2014): Emerging practices and perspectives on Big Data analysis in economics: Bigger and better or more of the same. Big Data & Society July–December 2014: 1–10. http://journals.sagepub.com/doi/pdf/10.1177/2053951714536877 H Chen, RHL Chiang, VC Storey (2012): Business intelligence and analytics: From big data to big impact. MIS quarterly, Special Issue Business intelligence research. Vol. 36 No. 4, pp. 11651188/December 2012. Griffin, P. & A. Wright (2018). Commentaries on Big Data's Importance for Accounting and Auditing. Accounting Horizon 29:2, 377-379. Fan, J. (2014). Features of Big Data and sparsest solution in high confidence set. In Past, Present and Future of Statistical Science (X, Lin, C. Genest, D. L. Banks, G. Molenberghs, D. W. Scott, J.-L. Wang, Eds.), Chapman & Hall, New York, 507-523 Fan, J., Guo, S. and Hao, N. (2012). Variance estimation using refitted cross-validation in ultrahigh dimensional regression. J. R. Statist. Soc. B (2012) 74, Part 1, pp. 37–65. Reshef, D. et al. (2011). Detecting novel associations in large data sets. Science, 334(6062):151824. Varian, H. R. (2014). Big Data: New Tricks for Econometrics. J. Economic Perspectives, 28(2), 3– 28. Wijayatunga, P., Mase, S. & Nakamura, M., ‘Appraisal of Companies with Bayesian Networks’. (2006). International Journal of Business Intelligence and Data Mining, Vol. 1, No. 3, pp.329–346. doi: 10.1504/IJBIDM.2006.009138 www.inderscience.com/search/index.php?action=record&rec_id=9138&prevQuery=&ps=10 &m=or Wijayatunga, P. (2016). A geometric view on Pearson's correlation coefficient and a generalization of it to non-linear dependencies. Ratio Mathematica, 30, pp. 3–21. http://www.eiris.it/ratio_numeri/ratio_30_2016/RM_30_1.pdf