Tackling Big Data and Black Swans through Fractal Approach and Quantum Technology

Alessio Faccia, Leonardo Jose Mataruna-Dos-Santos, Hussein Muñoz Helú, Andressa Fontes Guimaraes-Mataruna

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

Abstract

Since the dawn of time, man has always tried to predict the future. Inserted in an environmental context, the knowledge of the variables that influenced his life allowed him to reap daily benefits and ultimately ensured his survival. Weather forecasts, bets on sports results, financial analysis, estimation of life span probabilities, to name just a few examples, are based on increasingly accurate estimates thanks to increasingly efficient statistical techniques and detection tools. Risk and uncertainty, however, although increasingly limited, represent an essential variable of any future event. The possibility of measuring and preventing (even if close to their occurrence) unlikely, but potentially catastrophic events, can determine extraordinary competitive advantages or even just guarantee the survival of a business or human existence. Unlikely events, but catastrophic, are the so-called "black swans"[1], and represent the nightmare of those who rely on the Gaussian approach, since, even if they fall into the tails of the bell, they represent a non-negligible threat. Studies on the black swan, especially after the events linked to the outbreak of the COVID-19 pandemic, have brought to light the so-called fractal approach that comes closest to the occurrence of most natural events. The analysis of big data, focused on the identification of the black swan, can follow different paths, in any case, the "normal"Gauss curve, as demonstrated, does not lend itself to this type of analysis, therefore most of the statistical tools which are based on this are not suitable for these analyses. This research highlights and tries to demonstrate how the fractal approach, combined with quantum technology, could really represent a great advance in the reliability of future predictions and the detection of black swans.

Original languageEnglish
Title of host publicationProceedings of the 2020 International Conference on Big Data in Management, ICBDM 2020
Publisher Association for Computing Machinery
Pages28-32
Number of pages5
ISBN (Electronic)9781450375061
DOIs
Publication statusPublished - 15 May 2020
Event2020 International Conference on Big Data in Management, ICBDM 2020 - Virtual, Online, United Kingdom
Duration: 15 May 202017 May 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2020 International Conference on Big Data in Management, ICBDM 2020
CountryUnited Kingdom
CityVirtual, Online
Period15/05/2017/05/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Big Data
  • Black Swans
  • Forecasting
  • Fractals
  • Quantum technology

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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