VOOL: A modular insight-based framework for vocalizing OLAP sessions

Abstract

OLAP streamlines the exploration of multidimensional data cubes by allowing decision-makers to build sessions of analytical queries via a “point-and-click” interaction. However, new scenarios are appearing in which alternative forms of user-system communication, based for instance on natural language, are necessary. To cope with these scenarios, we present VOOL, an extensible framework for the vocalization of the results of OLAP sessions. To avoid flooding the user with long and tedious descriptions, we choose to vocalize only selected insights automatically extracted from query results. Insights are quantitative and rich-in-semantics characterizations of the results of an OLAP query, and they also take into account the user’s intentions as expressed by OLAP operators. Firstly, they are extracted using statistics and machine learning algorithms; then an optimization algorithm is applied to select the most relevant insights respecting a limit on the overall duration of vocalization. Finally, the selected insights are sorted into a comprehensive description that is vocalized to the user. After describing and formalizing our approach, we evaluate it from the points of view of efficiency, effectiveness, and operativity, also by comparing it with LLM-based applications.