Big Data Analytics Using Cloud and Crowd

TL;DR

This paper explores the integration of human crowd computing with machine-based big data analytics, proposing a framework to address challenges like security and privacy, and outlining future research directions.

Contribution

It introduces a hybrid human-machine framework for big data analytics and discusses open issues and future research directions in crowd-enabled data analysis.

Findings

Identifies challenges in crowd-based big data analytics

Proposes a framework for hybrid human-machine systems

Suggests future research directions in the field

Abstract

The increasing application of social and human-enabled systems in people's daily life from one side and from the other side the fast growth of mobile and smart phones technologies have resulted in generating tremendous amount of data, also referred to as big data, and a need for analyzing these data, i.e., big data analytics. Recently a trend has emerged to incorporate human computing power into big data analytics to solve some shortcomings of existing big data analytics such as dealing with semi or unstructured data. Including crowd into big data analytics creates some new challenges such as security, privacy and availability issues. In this paper study hybrid human-machine big data analytics and propose a framework to study these systems from crowd involvement point of view. We identify some open issues in the area and propose a set of research directions for the future of big data analytics area.