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

Abstract

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.

Evolutionary Optimization for Prioritized Materialized View Selection

“…1. Fitness cost (FC) (Gosain & Madaan, 2018b): The global minimum fitness cost computed using Equation 2. This incorporates both query processing cost and view maintenance cost.…”

“…4. High Priority% (HP%) (Gosain & Madaan, 2018b): This metric evaluates the percentage of highpriority queries answered by the materialized view set: higher the ratio, better the selection.…”

“…Thus, queries hold priority values depending on the users. The prioritized materialized view selection problem (PMVS) (Gosain & Madaan, 2018b) leverages this concept to prioritize cubes based on the query priority and provides a higher chance of selecting the cubes/views required by the top users. Furthermore, the authors proposed a priority and frequency-based cost model (PFBCM) by adding view priority into the fitness function for optimization.…”

“…Recently, Gosain & Madaan (2018a, 2018b introduced the idea of prioritizing cubes for selection. They took advantage of query priority based on different users in an organization and leveraged it further to assign importance to views.…”

“…The particle swarm optimization (PSO) algorithm has been utilized to solve PMVS and has produced satisfactory results (Gosain & Madaan, 2018b). Recently, naive evolutionary algorithms such as coral reef optimization (CRO) (Sanz et al, 2014), grey wolf optimizer (GWO) (Mirjalili et al, 2014), artificial bee colony (ABC) (Karaboga & Basturk, 2007), ant colony optimization (ACO) (Dorigo et al,2006), and cuckoo search (CS) (Yang & Deb, 2009) are rapidly adopted in diverse areas for efficient optimization.…”