Current business database systems utilize histograms to approximate frequency distributions of attribute values of relations. These are used to efficiently estimate query result sizes and access plan costs and thus minimize the query response time for database systems. In two recent works [7,8] we proposed (and thoroughly analyzed) two new forms of histogram-like techniques called the Rectangular and Trapezoidal Attribute Cardinality Maps (ACM) respectively, that give much smaller estimation errors than the traditional equi-width and equi-depth histograms currently being used by many commercial database systems. This paper reports how the benchmarking of the Rectangular-ACM (R-ACM) and the Trapezoidal-ACM (T-ACM) for query optimization can be achieved. By conducting an extensive set of experiments using the acclaimed TPC-D benchmark queries and database [15], we demonstrate that these new ACM schemes are much more accurate than the traditional histograms for query result size estimation.