Learning measures of semi-additive behaviour

TL;DR

This paper proposes a feature-based, case-based reasoning method to automatically determine the appropriate semi-additive aggregation behavior of measures in business analytics, achieving high accuracy.

Contribution

It introduces a novel set of features and a reasoning approach to automate measure aggregation decisions, reducing reliance on human expertise.

Findings

Achieved 86% accuracy in predicting aggregation behavior.

Developed a small feature set for measure behavior classification.

Demonstrated effectiveness in a real-world dataset.

Abstract

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.