A Survey on Domain-Specific Languages for Machine Learning in Big Data

TL;DR

This survey reviews domain-specific languages and frameworks tailored for machine learning in Big Data, aiding engineers and beginners in understanding the main tools and their applications in this rapidly evolving field.

Contribution

It provides a comprehensive overview of domain-specific languages for machine learning in Big Data, highlighting their features and usage to inform better decision-making.

Findings

Identifies key domain-specific languages used in Big Data machine learning.

Summarizes the advantages and limitations of these languages.

Provides guidance for software engineers and beginners in selecting appropriate tools.

Abstract

The amount of data generated in the modern society is increasing rapidly. New problems and novel approaches of data capture, storage, analysis and visualization are responsible for the emergence of the Big Data research field. Machine Learning algorithms can be used in Big Data to make better and more accurate inferences. However, because of the challenges Big Data imposes, these algorithms need to be adapted and optimized to specific applications. One important decision made by software engineers is the choice of the language that is used in the implementation of these algorithms. Therefore, this literature survey identifies and describes domain-specific languages and frameworks used for Machine Learning in Big Data. By doing this, software engineers can then make more informed choices and beginners have an overview of the main languages used in this domain.