Query realaxation is one of the critical components for approximate query answering. Query relaxation has extensively been investigated in terms of categorical data; few studies, however, have been effectively establishes for both numerical and categorical data. In this articlem we develop a query relaxation method by exploiting hierarchial quantified data abstraction, and a novel methos for categorical data are effectively relaxed. We additinally introduce query relaxation algorithms to modify the approximate queries into ordinary queries, which are followed by a series of examples to represent the modification process. Our methos outperformes the conventional approaches fot he various combinations of comples queries with respect to the cost model and the number of child nodes.