In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multidimensional 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, we propose a framework to predict the most likely next query and recommend this to the user. Our framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric. To gain insight in the recommendation accuracy and on what parameters it depends, we evaluate our approach using different quality assessments.
Predicting Your Next OLAP Query Based on Recent Analytical Sessions / M. Aufaure;N. Kuchmann-Beauger;P. Marcel;S. Rizzi;Y. Vanrompay. - STAMPA. - 8057:(2013), pp. 134-145. (Intervento presentato al convegno 15th International Conference on Data Warehousing and Knowledge Discovery tenutosi a Prague, Czech Republic nel August 26-29, 2013) [10.1007/978-3-642-40131-2].
Predicting Your Next OLAP Query Based on Recent Analytical Sessions
RIZZI, STEFANO;
2013
Abstract
In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multidimensional 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, we propose a framework to predict the most likely next query and recommend this to the user. Our framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric. To gain insight in the recommendation accuracy and on what parameters it depends, we evaluate our approach using different quality assessments.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.