A Historical Context for Data Streams
Indre Zliobaite Jesse Read
Machine learning from data streams is an active and growing research area.
Research on learning from streaming data typically makes strict assumptions
linked to computational resource constraints, including requirements for stream
mining algorithms to inspect each instance not more than once and be ready to
give a prediction at any time. Here we review the historical context of data
streams research placing the common assumptions used in machine learning over
data streams in their historical context.