View selection is important in many data-intensive systems e.g., commercial database and data warehousing systems. Given a database (or a data warehouse) schema and a query workload, view selection is to choose an appropriate set of views to be materialized that optimizes the total query cost, given a limited amount of resource, e.g., storage space and total view maintenance cost. The view selection problem is known to be a NP-complete problem. In this paper, we propose a declarative approach that involves a constraint programming technique which is known to be e cient for the resolution of NP-complete problems. The originality of our approach is that it provides a clear separation between formulation and resolution of the problem. For this purpose, the view selection problem is modeled as a constraint satisfaction problem in an easy and declarative way. Then, its resolution is performed automatically by the constraint solver. Furthermore, our approach is exible and extensible, in that it can easily model and handle new constraints and new heuristic search strategies to reduce the solution space. The performance results show that our approach outperforms the genetic algorithm which is known to provide the best trade-o between quality of solutions in terms of cost saving and execution time.