Born to be big: Data, graphs, and their entangled complexity

Enrico  Capobianco

doi:10.3934/bdia.2016002

Article Contents

2016, Volume 1, Issue 2&3: 163-169. Doi: 10.3934/bdia.2016002

This issue Previous Article A review on low-rank models in data analysis Next Article Time series based urban air quality predication

Born to be big: Data, graphs, and their entangled complexity

Enrico Capobianco^1,

1.
Center for Computational Science, University of Miami, Miami, FL 33146

Received: May 2016

Revised: July 2016

Published: August 2016

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Abstract

Big Data and Big Graphs have become landmarks of current cross-border research, destined to remain so for long time. While we try to optimize the ability of assimilating both, novel methods continue to inspire new applications, and vice versa.Clearly these two big things, data and graphs, are connected, but can we ensure management of their complexities, computational efficiency, robust inference? Critical bridging features are addressed here to identify grand challenges and bottlenecks.

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Mathematics Subject Classification: Primary: 68Qxx; Secondary: 81P40.

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