Exploiting CoDe modeling for the optimization of OLAP queries

The visualization of big-data represents a hard challenge due to the sheer amount of information contained in data warehouses. Thus, the accuracy on data relationships in a representation becomes one of the most crucial aspects to perform business knowledge discovery. A tool that allows to model and visualize information relationships between data is CoDe, which by processing several queries on a data-mart, generates a visualization of such data. However on a large data warehouse, the computation of these queries increases the response time by the query complexity. A common approach to speed up data warehousing is precompute a set of materialized views, store in the warehouse and use them to compute the workload queries. In this paper, we define a process exploiting the CoDe modeling to determine the minimal number of required OLAP queries and to mitigate the problem of view selection, i.e., select the optimal set of materialized views. The results of an experiment on a real data warehouse show an improvement in the range of 62-98% with respect the approach that does not consider materialized views, and 5% wrt. an approach that exploits them