Learning measures of semi-additive behaviour
This work addresses the need for automated aggregation suggestions in business analytics systems, but it is incremental as it builds on existing case-based reasoning methods with a small feature set.
The paper tackled the problem of automatically determining the appropriate aggregation behavior (e.g., sum, mean) for measures in business analytics, which is typically done manually by experts, and achieved 86% accuracy on a collected dataset using a case-based reasoning approach.
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.