Learning measures of semi-additive behaviour
  In business analytics, measure values, such as sales numbers or volumes of
cargo transported, are often summed along values of one or more corresponding
categories, such as time or shipping container. However, not every measure
should be added by default (e.g., one might more typically want a mean over the
heights of a set of people); similarly, some measures should only be summed
within certain constraints (e.g., population measures need not be summed over
years). In systems such as Watson Analytics, the exact additive behaviour of a
measure is often determined by a human expert. In this work, we propose a small
set of features for this issue. We use these features in a case-based reasoning
approach, where the system suggests an aggregation behaviour, with 86% accuracy
in our collected dataset.
