Decision makers often view aggregate data in a data warehouse via multidimensional data cubes. In relational databases, we refer a data cube as a set of views. In order to improve the query performance against the data cube, the common technique used is to materialize some of the views in the data cube. Once a view is chosen to be materialized, the system manager must consider its implementation and maintenance cost. Because of space limit, it is important to select the right set of views in the data cube to materialize that improve query performance and reduce the maintenance cost. In this thesis, we investigate previous works on the selection of materialized views in a data warehouse, and design a backward greedy algorithm which solve the problem of selecting materialized views in data cubes under space constraint. Unlike previous algorithms, we evaluate each view by calculating its damage to the overall performance. In addition to provide a different selection strategy for system managers to satisfy their need, in some cases backward greedy algorithm provides a better view selection than previous algorithms. We also combine our algorithm with previous algorithms to further improve the results of view selection.