Paper Info
Title
Towards Model-Driven Engineering for Big Data Analytics -- An Exploratory Analysis of Domain-Specific Languages for Machine Learning
Abstract
Graphical models and general purpose inference algorithms are powerful tools for moving from imperative towards declarative specification of machine learning problems. Although graphical models define the principle information necessary to adapt inference algorithms to specific probabilistic models, entirely model-driven development is not yet possible. However, generating executable code from graphical models could have several advantages. It could reduce the skills necessary to implement probabilistic models and may speed up development processes. Both advantages address pressing industry needs. They come along with increased supply of data scientist labor, the demand of which cannot be fulfilled at the moment. To explore the opportunities of model-driven big data analytics, I review the main modeling languages used in machine learning as well as inference algorithms and corresponding software implementations. Gaps hampering direct code generation from graphical models are identified and closed by proposing an initial conceptualization of a domain-specific modeling language.
Year
DOI
Venue
2014
10.1109/HICSS.2014.101
System Sciences
Keywords
Field
DocType
data scientist labor,big data analytics,direct code generation,domain-specific modeling language,exploratory analysis,graphical model,executable code,main modeling language,inference algorithm,towards model-driven engineering,machine learning,domain-specific languages,development process,general purpose inference algorithm,model-driven big data analytics,computer graphics,model driven engineering,data analysis,graphical models,big data,learning artificial intelligence
Data science,Domain-specific language,Computer science,Inference,Model-driven architecture,Modeling language,Code generation,Artificial intelligence,Graphical model,Big data,Machine learning,Executable
Conference
ISSN
Citations 
PageRank 
1060-3425
7
0.52
References 
Authors
11
1
Name
Order
Citations
PageRank
Dominic Breuker19013.34