Efficient approach for view materialisation in a data warehouse by prioritising data cubes

Selecting an appropriate set of views for materialisation is an important problem in a data warehouse, and is referred to as the view selection problem. The existing state-of-the-art cost models select a set of views based on parameters, such as query frequency, view size, view update frequency, and view update costs. The existing methods do not consider query priority as a parameter for selecting views that can lead to shorter query processing times. Thus, in this paper, 'priority' is selected as a new selection parameter. Priority values are assigned to each query per user requirements, as well as using query type, user's level, and department preference in an organisation. As analytical queries require aggregated data cubes, priority values are assigned to each data cube based on priority value of the queries accessing them. Finally, a modified cost model is designed that integrates cube priority along with other selection parameters. The authors' proposed model uses the particle swarm optimisation algorithm for selecting a set of prioritised cubes by minimising the total query running cost under storage constraints. The experimental results shows that the proposed cost model leads to better cube selection, and consequently, shorter query running times.