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Requirements for database systems differ from small-scale database programs for embedded devices with minimal footprint to large-scale on-line analytical processing applications. For relational database management systems, two storage architectures have been introduced: a) row-oriented architecture and b) column-oriented architecture. In this paper, we present a query decomposition approach to evaluate database operations with respect to their performance according to the storage architecture. We map decomposed queries to workload patterns which contain aggregated database statistics. Further, we develop our complementary decision models which advise the selection of the optimal storage architecture for a given application domain. The first decision model improves the performance of running systems (on-line). The second and third decision model advise an efficient database design or decide which architecture is more suitable for a given application domain (off-line).