Paper Info
Title
A Preliminary Survey on Domain-Specific Languages for Machine Learning in Big Data
Abstract
The proliferation of data often called Big Data has created problems with traditional approaches to data capture, storage, analysis and visualization, thus opening up new areas of research. Machine Learning algorithms are one area that has been used in Big Data for analysis. 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. This literature survey identifies and describes domain-specific languages and frameworks used for Machine Learning in Big Data with the intention of assisting software engineers in making more informed choices and providing beginners with an overview of the main languages used in this domain. This is the first survey that aims at better understanding how domain-specific languages for Machine Learning are used as a tool for research in Big Data.
Year
DOI
Venue
2016
10.1109/SWSTE.2016.23
2016 IEEE International Conference on Software Science, Technology and Engineering (SWSTE)
Keywords
Field
DocType
literature survey,domain-specific languages,DSL,Machine Learning,ML,Big Data,BD
Data science,Data mining,Active learning (machine learning),Computer science,Software,Artificial intelligence,Domain-specific language,Second-generation programming language,Software engineering,Visualization,Automatic identification and data capture,Big data,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5090-1019-6
7
0.57
References 
Authors
8
3
Name
Order
Citations
PageRank
Ivens Portugal1291.99
Paulo S. C. Alencar239345.89
Donald D. Cowan358190.75