The Role of Machine Learning in Big Data Analytics: Current Practices and Challenges

A massive amount of data is generated at an ever-increasing rate. Social media, mobile phones, sensors, and medical imaging, among others, are examples of data sources. An important characteristic of the data generated by these sources is that the data is commonly either unstructured or semi-structured. Big data analytics comprises software systems that are able to analyze vast amounts of data to uncover information such as patterns and correlations that help decision-makers in making better decisions. Traditional approaches such as data warehousing and the use of a classic relational database management system (RDBMS) have become impractical to analyze such unstructured and semi-structured data. On the other hand, machine learning (ML) algorithms have proven to be successful in analyzing such vast amounts of data. In this chapter, we present some of the most widely used ML algorithms in big data analytics as well as the distributed platforms typically employed for processing the data. We also present a selection of three important application domains where ML algorithms have been applied to perform big data analytics. These application domains include healthcare, weather forecasting, and social networking. Finally, we review relevant approaches used in each domain area, the most commonly used ML algorithms per area, and specific domain area issues that need further research in big data analytics.