Download Big Data literature

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