Using relational database constraints to design materialized views in data warehouses

Queries to data warehouses often involve hundreds of complex aggregations over large volumes of data, and so it is infeasible to compute these queries by scanning the data sources each time. Data warehouses therefore build a large number of materialized views to increase system performance. However, materialized views need to be immediately updated when its sources are changed, leading to a possible decrease in system performance. The goal of the materialized view selection problem is to select an appropriate set of views that minimize total query response time as well as the view maintenance cost. In this paper, we develop a solution for identifying the candidate view space of materialization. In particular, we present algorithms for generating a union-view and a partial-view, both of which are good candidates for materialization. The proposed candidate view space guarantees to find a polynomial bounded set of optimal views, and any selection algorithm from previous research, e.g. greedy algorithm, can be ran on the candidate view space to find the optimal materialized views.