State of art streaming learning I have been working with large data sets lately and found a lot of papers of streaming methods. To name a few:


*

*Follow-the-Regularized-Leader and Mirror Descent:
Equivalence Theorems and L1 Regularization
(http://jmlr.org/proceedings/papers/v15/mcmahan11b/mcmahan11b.pdf)

*Streamed Learning: One-Pass SVMs (http://www.umiacs.umd.edu/~hal/docs/daume09onepass.pdf)

*Pegasos: Primal Estimated sub-GrAdient SOlver for SVM http://ttic.uchicago.edu/~nati/Publications/PegasosMPB.pdf

*or here : Can SVM do stream learning one example at a time?

*Streaming Random Forests (http://research.cs.queensu.ca/home/cords2/ideas07.pdf)


However, I have been unable to find any documentation regarding how they compare to each other. Every article I read seem to run experiments on different data set.
I know about sofia-ml, vowpal wabbit, but they seem to implement very few methods, compared to the huge amount of existing methods! 
Are the less common algorithms not performant enough? Is there any paper trying to review as many methods as possible?
 A: A rigorous survey of multiple algorithms similar to the Delgado paper you linked is not available as far as I know, but there have been efforts to gather results for families of algorithms.
Here are some sources I find useful (disclaimer: I publish in the area, so it's likely I'm biased in my selection):


*

*A survey on Ensemble Learning for Data Stream Classification (Survey)

*Online Learning and Online Convex Optimization (Technical Report)

*Online Machine Learning in Big Data Streams (Survey)

*Machine Learning for Data Streams (Book)

*Algorithms for Learning Regression Trees and Ensembles on Evolving Data Streams (PhD Thesis)

*Learning under Concept Drift: an Overview (Survey)

*Optimal and Adaptive Online Learning (PhD Thesis)

*Adaptive Learning and Mining for Data Streams and Frequent Patterns (PhD Thesis)


Some sofware packages:


*

*MOA and SAMOA

*scikit-multiflow

*Jubatus

*LIB(S)OL

*StreamDM
I can add more info and sources if needed. As others have said the field could use a comprehensive survey.
