Searching Big Data via cyclic groups
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Published
Apr 16, 2017
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Abstract
We look up for a certain information in big data. To achieve this task we first endow the big data with a group structure and partition it to it’s cyclic subgroups. We devise a method to search the whole big data starting from the smallest subroup through the largest one. Our method eventually exhausts the whole big data.
Keywords: BigData, topological groups, dual groups, linear search.
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References
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[2] Rudin, W. Fourier Analysis on Groups. New York: Interscience, 1962.
[3] OEIS. 2016. Available from: http://oeis.org/.
[4] Calvin T. L. Elementary Introduction to Number Theory, 2nd ed., Lexington: D. C. Heath and Company, LCCN 77-171950, 1972.
[5] Anthony J. P. and Donald R. B. Elements of Number Theory. Englewood Cliffs: Prentice Hall, LCCN 77-81766, 1970.
[6] Abramowitz, M. The Euler Totient Function. In Stegun, C. A. (ed.). 24.3.2 Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 9th edition. New York: Dover, p. 826, 1972.
[7] Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D. And Tufano, P. Analytics: The real-world use of big data. How innovative enterprises extract value from uncertain data IBM Institute for Business Value, 2012.
[8] Diebold, F.X. A personal perspective on the origin(s) and development of big data: The phenomenon, the term, and the discipline (Scholarly Paper No. ID2202843)
[9] Chung, W. BizPro: Extracting and categorizing business intelligence factors from textual news articles. International Journal of Information Management, 2014, 34 (2), pp. 272-284
[10] Jiang, J. Information extraction from text. In: Aggarwal, C.C. and Zhai, C. (ed.) Mining text data United States: Springer, 2012, pp. 1141