  In this paper we propose an extension of the notion of deviation-based
aggregation function tailored to aggregate multidimensional data. Our objective
is both to improve the results obtained by other methods that try to select the
best aggregation function for a particular set of data, such as penalty
functions, and to reduce the temporal complexity required by such approaches.
We discuss how this notion can be defined and present three illustrative
examples of the applicability of our new proposal in areas where temporal
constraints can be strict, such as image processing, deep learning and decision
making, obtaining favourable results in the process.
