Big Data: Unleashing information

Journal of Systems Science and Systems Engineering - Tập 22 - Trang 127-151 - 2013
James M. Tien1
1College of Engineering, University of Miami, Coral Gables, USA

Tóm tắt

At present, it is projected that about 4 zettabytes (or 10**21 bytes) of digital data are being generated per year by everything from underground physics experiments to retail transactions to security cameras to global positioning systems. In the U. S., major research programs are being funded to deal with big data in all five sectors (i.e., services, manufacturing, construction, agriculture and mining) of the economy. Big Data is a term applied to data sets whose size is beyond the ability of available tools to undertake their acquisition, access, analytics and/or application in a reasonable amount of time. Whereas Tien (2003) forewarned about the data rich, information poor (DRIP) problems that have been pervasive since the advent of large-scale data collections or warehouses, the DRIP conundrum has been somewhat mitigated by the Big Data approach which has unleashed information in a manner that can support informed — yet, not necessarily defensible or valid — decisions or choices. Thus, by somewhat overcoming data quality issues with data quantity, data access restrictions with on-demand cloud computing, causative analysis with correlative data analytics, and model-driven with evidence-driven applications, appropriate actions can be undertaken with the obtained information. New acquisition, access, analytics and application technologies are being developed to further Big Data as it is being employed to help resolve the 14 grand challenges (identified by the National Academy of Engineering in 2008), underpin the 10 breakthrough technologies (compiled by the Massachusetts Institute of Technology in 2013) and support the Third Industrial Revolution of mass customization.

Tài liệu tham khảo

Ahlquist, J. & Saagar, K. (May/June 2013). Comprehending the complete customer. Analytics Magazine, 36–50 Allen, B., Bresnahan, J., Childers, L., Foster, I., Kandaswamy, G., Kettimuthu, R., Kordas, J., Link, M., Martin, S., Pickett, K. & Tuecke, S. (2012). Software as a service for data scientists. Communications of the ACM, 55(2): 81–88 Appel, K. & Haken, W. (October 1977). Solution of the four color map problem. Scientific American, 237(4): 108–121 Barbot, S., Lapusta, N. & Avouac, J.-P. (2012). Under the hood of the earthquake machine: toward predictive modeling of the seismic cycle. Science, 336: 707–710 Baru, C., Bhandarkar, M., Nambiar, R., Poess, M. & Rabl, T. (March 2013). Benchmarking big data systems and the bigdata top 100 list. Big Data, 60-64 Bizer, C., Boncz, P., Bodie, M.L. & Erling, O. (December 2011). The meaningful use of big data: four perspectives — four challenges. SIGMOD Record, 40(4): 56–60 Black, F. & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81: 637–654 Carr, N. (2010). The Shallows: What the Internet Is Doing to Our Brains. Norton, New York, NY Chen, H., Chiang, R.H.L. & Storey, V.C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4): 1165–1188 Davenport, T.H. & Harris, J.G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press, Cambridge, MA Futardo, P. (2009). A survey of parallel and distributed data warehouses. International Journal of Data Warehousing and Mining, 5(2): 57–77 Hattori, H., Nakajima, Y. & Ishida, T. (2011). Learning from humans: agent modeling with individual human behaviors. IEEE Transactions on Systems, Man and Cybernetics — Part A, 41(1): 1–9 Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52(8): 36–44 Lavalle, S., Lesser, E., Shockley, R., Hopkins, M.S. & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2): 21–31 Luhn, H.P. (1958). A business intelligence system. IBM Journal, 2(4): 314–350 Manyika, J., Chui, M., Bughin, J., Brown, B., Dobbs, R., Roxbury, C. & Byers, A.H. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, New York, NY Mayer-Schonberger, V. & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work and Think. Houghton Mifflin Harcourt Publishing Company, New York, NY McAfee, A. & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard Business Review, October, 3–9 McFadden, D.L. (2013). The new science of pleasure. National Bureasu of Economic Research. Working paper 18687 Mlodinow, L. (2012). Subliminal: How Your Unconscious Mind Rules Your Behavior. Pantheon Books, New York, NY Rifkin, J. (2011). The Third Industrial Revolution: How Lateral Power Is Transforming Energy, the Economy, and the World. Palgrave Macmillan, New York, NY Samuelson, D.A. (2013). Analytics: key to Obama’s victory. OR/MS Today, February, 20–24 Schadt, E.E., Linderman, M.D., Sorenson, J., Lee, L. & Nolan, G.P. (2010). Computational solutions to large-scale data management and analysis. Nature Reviews, 11: 647–657 Segall, P. (2012). Understanding earthquakes. Science, 336: 676–710 Shostack, A. (2012). The evolution of information security. The Next Wave, 19(2): 6–11 Siegel, E. (2013). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. John Wiley & Sons, New York, NY Silver, N. (2003). Introducing PECOTA. In: Huckabay, G., Kahrl, C., Pease, D. (eds.), Baseball Prospectus 2003, pp. 507–514. Dulles, VA: Brassey’s Publishers Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail — But Some Don’t. The Penguin Press, New York, NY Stern, A. & Lindner, N.H. (8 March 2013). Topological quantum computation — from basic concepts to first experiments. Science, 339: 1179–1184 Swart, E.R. (1980). The philosophical implications of the four-color problem. American Mathematical Monthly, 87(9): 697–702 Taleb, N.N. (2010). The Black Swan: Second Edition. Random House, Inc., New York, NY Tien, J.M. (2003). Toward a decision informatics paradigm: a real-time information based approach to decision making. IEEE Transactions on Systems, Man and Cybernetics, Part C, Special Issue, 33(1): 102–113 Tien, J.M. (2008). On integration and adaptation in complex service systems. Journal of Systems Science and Systems Engineering, 17(2): 1–31 Tien, J.M. (2011). Manufacturing and services: from mass production to mass customization. Journal of Systems Science and Systems Engineering, 20(2): 129–154 Tien, J.M. (2012). The next industrial revolution: integrated services and goods. Journal of Systems Science and Systems Engineering, 21(3): 257–296 Tien, J.M. & Berg, D. (1995). Systems engineering in the growing service economy. IEEE Transactions on Systems, Man, and Cybernetics, 25(5): 321–326 Tien, J.M. & Berg, D. (2003). A case for service systems engineering. International Journal of Systems Engineering, 12(1): 13–39 Tien, J.M., Krishnamurthy, A. & Yasar, A. (2004). Towards real time management of supply and demand chains. Journal of Systems Science and Systems Engineering, 13(3): 257–278 Tien, J.M. & McClure, J.A. (1986). Towards an operations-oriented approach to information systems design in public organizations. Public Administration Review, Special Issue on Public Managemnt Information Systems, 27(7): 553–562 Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59: 433–460 van Hattum, P. & Hoijtink, H. (2008). Data fusion: an application in marketing. Database Marketing & Customer Strategy Management, 15(4): 267–284 Wilson, R. (2002). Four Colors Suffice. Penguin Books, London, England Zikopoulos, P.C., Eaton, C., DeRoos, D., Deutsch, T. & Lapis, G. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. The McGraw-Hill Companies, New York, NY