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 datawarehouse, and is referred to as the view selection problem. The existingstate‐of‐the‐art cost models select a set of views based on parameters, such asquery frequency, view size, view update frequency, and view update costs. Theexisting methods do not consider query priority as a parameter for selectingviews that can lead to shorter query processing times. Thus, in this paper,'priority’ is selected as a new selection parameter. Priority values areassigned to each query per user requirements, as well as using query type,user's level, and department preference in an organisation. As analyticalqueries require aggregated data cubes, priority values are assigned to each datacube based on priority value of the queries accessing them. Finally, a modifiedcost model is designed that integrates cube priority along with other selectionparameters. The authors’ proposed model uses the particle swarm optimisationalgorithm for selecting a set of prioritised cubes by minimising the total queryrunning cost under storage constraints. The experimental results shows that theproposed cost model leads to better cube selection, and consequently, shorterquery running times.