# Big Data Analytics = Machine Learning + Cloud Computing

arXiv: https://arxiv.org/abs/1601.03115 · HTML: https://mlsanity.com/paper/1601.03115/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2016.

## Built on by (most cited first, 1 of 1)

- [A Systematic Literature Review on the Use of Machine Learning in Software Engineering](https://mlsanity.com/paper/2406.13877/) (2024)

## Abstract (arXiv)

Big Data can mean different things to different people. The scale and challenges of Big Data are often described using three attributes, namely Volume, Velocity and Variety (3Vs), which only reflect some of the aspects of data. In this chapter we review historical aspects of the term "Big Data" and the associated analytics. We augment 3Vs with additional attributes of Big Data to make it more comprehensive and relevant. We show that Big Data is not just 3Vs, but 32 Vs, that is, 9 Vs covering the fundamental motivation behind Big Data, which is to incorporate Business Intelligence (BI) based on different hypothesis or statistical models so that Big Data Analytics (BDA) can enable decision makers to make useful predictions for some crucial decisions or researching results. History of Big Data has demonstrated that the most cost effective way of performing BDA is to employ Machine Learning (ML) on the Cloud Computing (CC) based infrastructure or simply, ML + CC -> BDA. This chapter is devoted to help decision makers by defining BDA as a solution and opportunity to address their business needs.