An effective query optimizer finds a query plan that exploits the characteristics of the source data. In data integration, little is known in advance about sources’ properties, which necessitates the use of adaptive query processing techniques to adjust query processing on-the-fly. Prior work in adaptive query processing has focused on compensating for delays and adjusting for mis-estimated cardinality or selectivity values. In this paper, we present a generalized architecture for adaptive query processing and introduce a new technique, called adaptive data partitioning (ADP), which is based on the idea of dividing the source data into regions, each executed by different, complementary plans. We show how this model can be applied in novel ways to not only correct for underestimated selectivity and cardinality values, but also to discover and exploit order in the source data, and to detect and exploit source data that can be effectively pre-aggregated. We experimentally compare a number of alternative strategies and show that our approach is effective.