University of Bochum
In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multi-dimensional data cubes. Being able to predict the most likely next queries would provide a way to recommend interesting queries to users on the one hand, and could improve the efficiency of OLAP sessions on the other. In particular, query recommendation would proactively guide users in data exploration and improve the quality of their interactive experience. In this paper, the authors propose a framework to predict the most likely next query and recommend this to the user. Their framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric.