Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

UNDERSTANDING AI IN A WORLD OF BIG DATA

Environics Analytics, 33 Bloor St. East, Toronto, Ont. M4W3H1, Canada

Big Data and AI are now very popular concepts within the public lexicon. Yet, much confusion exists as to what these concepts actually mean and more importantly why they are significant forces within the world today. New tools and technologies now allow better access as well as facilitating the analysis of this data for better decision-making. But the discipline of data science with its four-step process in conducting any analysis is the key towards success in both non-advanced and advanced analytics which would, of course, include the use of AI. This paper attempts to demystify these concepts from a data science perspective. In attempting to understand Big Data and AI, we look at the history of data science and how these more recent concepts have helped to optimize solutions within this 4 step process.
  Figure/Table
  Supplementary
  Article Metrics

References

[1] Figure.1: The 5 V's of big data, Environics Analytics: Best Practices and Considerations in Big Data Analytics, June, 2018.

[2] Figure.2: Moore's Law, https://www.google.ca/search?hl=en&tbm=isch&source=hp&biw=1366&bih=651&ei=wd3pWuPdMqqPjwSUr4SQDQ&q=exponential+growth+in+computing+power&oq=growth+in+computing+power&gs_l=img.1.1.0j0i5i30k1.4574.14021.0.16389.28.27.0.1.0.0.182.2657.16j10.26.0....0...1ac.1.64.img..1.25.2467.0..0i24k1j0i8i30k1.0.eDlGB4j2AdI#imgrc=jhm-BdlhnmB2HM:.

[3] Figure.3: Columnar file formats, https://www.google.ca/search?hl=en&tbm=isch&source=hp&biw=1366&bih=651&ei=wd3pWuPdMqqPjwSUr4SQDQ&q=exponential+growth+in+computing+power&oq=growth+in+computing+power&gs_l=img.1.1.0j0i5i30k1.4574.14021.0.16389.28.27.0.1.0.0.182.2657.16j10.26.0....0...1ac.1.64.img..1.25.2467.0..0i24k1j0i8i30k1.0.eDlGB4j2AdI#imgrc=jhm-BdlhnmB2HM:.

[4] Index compression, https://nlp.stanford.edu/IR-book/html/htmledition/index-compression-1.html.

[5] Figure.6-Sequential vs. parallel data processing, https://www.google.ca/search?biw=1607&bih=678&tbm=isch&sa=1&ei=UVPwWu_uGoeYjwSkqrjwBA&q=sequential+db+processing&oq=sequential+db+processing&gs_l=img.3...0.0.0.123836.0.0.0.0.0.0.0.0..0.0....0...1c..64.img..0.0.0....0.jkNEKg1fCW0#imgdii=kH8ag2orN-LWNM:&imgrc=pBOBcUMsqlXNGM:&spf=1525699534175.

[6] Turn to in-memory processing when performance matters, https://searchdatacenter.techtarget.com/feature/Turn-to-in-memory-processing-when-performance-matters.

[7] Figure.8: Schematic of weights within neural net structure, https://www.google.ca/search?hl=en&tbm=isch&source=hp&biw=1366&bih=651&ei=bpvwWv2FM82O5wLqzLigCA&q=neural+net+simple+network&oq=neural+net+simple+network&gs_l=img.3...1065.21853.0.22452.38.24.0.14.14.0.120.1836.21j2.23.0....0...1ac.1.64.img..1.13.1052.0..0j0i24k1j0i10i24k1j0i10k1j0i7i30k1.0.nu7gREvNHkk#imgrc=13gO7BFb0GYZqM:.

[8] Figure. 9-Examples of some optimization algorithms, https://www.google.ca/search?hl=en&tbm=isch&q=logistic+function&chips=q:logistic+function,g_5:logistical&sa=X&ved=0ahUKEwjw-KD5oPTaAhWkpFkKHSxSDJwQ4lYIMCgA&biw=1366&bih=651&dpr=1#imgrc=oAHIGiD5uTjw2M: https://www.google.ca/search?hl=en&tbm=isch&q=tan+function+graph&chips=q:tan+function+graph,g_1:tangent,online_chips:cos+tan&sa=X&ved=0ahUKEwjK-IGYovTaAhVQwlkKHUBnC0cQ4lYIKygC&biw=1366&bih=651&dpr=1#imgrc=gWnErav-9CIbGM:.

[9] "Is predictive analytics for marketers really that accurate?", Journal of Marketing Analytics, May, 2013. https://link.springer.com/article/10.1057/jma.2013.8.

[10] "Data Mining for Managers: How to use data (big and small) to solve business problems", by Palgrave Macmillan, Oct, 2014.

© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved