A Survey on Domain-Specific Languages for Machine Learning in Big Data
It addresses the problem of language selection for machine learning in big data, which is incremental as it compiles existing information without introducing new methods.
This survey identifies and describes domain-specific languages and frameworks used for implementing machine learning algorithms in big data contexts, aiming to help software engineers make informed choices and provide beginners with an overview of the main languages in this domain.
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.