In the last two decades, machine learning research and practice has focused on batch learning,  usually with small datasets. 
Nowadays there are applications in which the data are modeled best not as persistent tables, but rather as transient data streams. 
Learning from data streams is an increasing research area with challenging applications and contributions from fields like data bases, learning theory, machine learning, and data mining. In this work we identify the main characteristics of stream mining algorithms, and present some illustrative examples of such algorithms.