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In this chapter, we explain what is the main problems with which we deal in this book, how exactly we deal with them, and what is the book’s structure.
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References
G. Aletti, A.K. Naimzada, G. Naldi, Mathematics and physics applications in sociodynamics simulation: the case of opinion formation and diffusion, in Mathematical Modeling of Collective Behavior in Socio-Economic and Life Sciences, ed. by G. Naldi, L. Pareschi, G. Toscani (Springer, Dordrecht, 2010), pp. 203–221
L.M.A. Bettencourt, A. Cintron-Arias, D.I. Kaiser, C. Castillo-Chavez, The power of a good idea: quantitative modeling of the spread of ideas from epidemiological models. Phys. A 364, 513–536 (2006)
L.M.A. Bettencourt, D.I. Kaiser, J. Kaur, C. Castillo-Chavez, D.E. Wojick, Population modeling of the emergence and development of scientific fields. Scientometr. 75(3), 495–518 (2008)
P.A. Geroski, Models of technology diffusion. Res. Policy 29, 603–625 (2000)
M. Gomez-Rodriguez, J. Leskovec, A. Krause, Inferring networks of diffusion and influence, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD’10, Washington, DC, 25–28 July 2010
M. Ingawale, Understanding the Wikipedia phenomenon: a case for agent based modeling, in Proceedings of the ACM Ph.D. Workshop on Information and Knowledge Management PIKM’08, Napa Valley, California, 30 Oct 2008, pp. 81–84
P. Morone, R. Taylor, Knowledge Diffusion and Innovation: Modeling Complex Entrepreneurial Behaviors (Edward Elgar Publication, Cheltenham, UK, 2010)
A. Pentland, Social Physics: How Good Ideas Spread—The Lessons from a New Science (Penguin Press, New York, 2014)
G. Rossman, Climbing the Charts: What Radio Airplay Tells Us about the Diffusion of Information (Princeton University Press, Princeton, New Jersey, 2012)
C.M. Tsai, The knowledge diffusion model associated with innovative knowledge. Expert Syst. Appl. 36, 11323–11331 (2009)
J. Xu, M. Quaddus, Examining a model of knowledge management systems adoption and diffusion: a partial Least Square approach. Knowl. Based Syst. 27, 18–28 (2012)
P. Yan, X. Yang, To trade or to teach: modeling tacit knowledge diffusion in complex social networks, in Proceedings of the Second International Conference on Future Information Technology and Management Engineering FITME’2009, Sanya, China, 13–14 Dec 2009, pp. 151–154
B. Baesens, Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (Wiley, Hoboken, New Jersey, 2014)
B. Marr, Big Data: Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance (Wiley, Chichester, UK, 2015)
N. Marz, J. Warren, Big Data: Principles and Best Practices of Scalable Realtime Data Systems (Manning Publication, Shelter Island, New York, 2015)
V. Mayer-Schönberger, K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think (John Murray Publication, London, UK, 2013)
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Lerma, L.O., Kreinovich, V. (2018). Introduction. In: Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data. Studies in Big Data, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-319-61349-9_1
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DOI: https://doi.org/10.1007/978-3-319-61349-9_1
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