The use of materialized views derived from the intermediate results of frequently executed queries is a popular strategy for improving performance in query workloads. Optimizers capable of matching such views with inbound queries can generate alternative execution plans that read the materialized contents directly instead of re-computing the corresponding subqueries, which tends to result in reduced query execution times. In this paper, we introduce an architecture called Hawc that extends a cost-based logical optimizer with the capability to use history information to identify query plans that, if executed, produce intermediate result sets that can be used to create materialized views with the potential to reduce the execution time of future queries. We present techniques for using knowledge of past queries to assist the query optimizer and match, generate and select useful materialized views. Experimental results indicate that these techniques provide substantial improvements in workload execution time.