Integrating usage analysis on cube view selection – An alternative method

Abstract

One of the best ways to make an effective selection of data cube views is based on monitoring multidimensional queries during a relevant number of OLAP sessions. This allows to understand how and when a data cube is explored, collecting and analysing the views that decision agents use to consult. This is very important, because it deals directly with the optimisation of resources, namely the ones related to storage capacity and query processing time. Based on this, in this paper, we propose a new view selection method – M3 – for cubes, based on the analysis of OLAP usage sessions. M3 operates on specialised information collected from multidimensional queries launched over one or more data cubes. The aim was to categorise OLAP usage and ensure that views to be materialised will be the ones corresponding to the most widely used and consulted by decision-makers, for a specific period of time.