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.
Selecting appropriate views that provide faster query response time is a critical decision in data warehouse design. Top-level users expect quick results from a data warehouse for faster decision-making to gain a competitive edge in business. Prioritizing a view can distinguish views required to answer top-level users’ queries from regular users and provide a better selection chance. The prioritized materialized view selection (PMVS) problem addresses how to utilize the given space to materialize prioritized views more relevant to users. Particle swarm optimization algorithm has been used to achieve minimized query processing costs. Evolutionary algorithms are widely known to solve complex optimization problems quickly by reaching a semi-optimal solution. This paper explores the performance of six evolutionary algorithms: particle swarm optimization, coral reef optimization, cuckoo search, ant colony optimization, grey wolf optimization, and artificial bee colony. The results of empirical and statistical analysis show that PSO, CRO, and GWO algorithms are best suited to solve PMVS.
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