  The literature on machine learning in the context of data streams is vast and
growing. However, many of the defining assumptions regarding data-stream
learning tasks are too strong to hold in practice, or are even contradictory
such that they cannot be met in the contexts of supervised learning. Algorithms
are chosen and designed based on criteria which are often not clearly stated,
for problem settings not clearly defined, tested in unrealistic settings,
and/or in isolation from related approaches in the wider literature. This puts
into question the potential for real-world impact of many approaches conceived
in such contexts, and risks propagating a misguided research focus. We propose
to tackle these issues by reformulating the fundamental definitions and
settings of supervised data-stream learning with regard to contemporary
considerations of concept drift and temporal dependence; and we take a fresh
look at what constitutes a supervised data-stream learning task, and a
reconsideration of algorithms that may be applied to tackle such tasks. Through
and in reflection of this formulation and overview, helped by an informal
survey of industrial players dealing with real-world data streams, we provide
recommendations. Our main emphasis is that learning from data streams does not
impose a single-pass or online-learning approach, or any particular learning
regime; and any constraints on memory and time are not specific to streaming.
Meanwhile, there exist established techniques for dealing with temporal
dependence and concept drift, in other areas of the literature. For the data
streams community, we thus encourage a shift in research focus, from dealing
with often-artificial constraints and assumptions on the learning mode, to
issues such as robustness, privacy, and interpretability which are increasingly
relevant to learning in data streams in academic and industrial settings.
