Partial indexes are access structures on the physical level of the databases. They are indexes that allow the definition of a subset of tuples in a table through a conditional expression. This dissertation studies the identification and subsequent automatic creation of partial indexes that can contribute in improving the performance of a database system. We propose an algorithm that examines, for each relevant query, the indexable attributes set, for which the creation of a partial index could influence the query optimizer to generate plans that are more efficient. We perform data mining on indexable attributes patterns to obtain correlated attributes according to their frequency in queries within the particular workload. We obtain a proposal for a set of candidate partial indexes considering also a benefit heuristics. We may consider a self-tuning analysis of an index configuration with both complete and partial indexes. We have implemented techniques and algorithms proposed in this research into DBX, a framework that allows local and global self-tuning regarding relational databases.