Single-criteria decision making queries can be answered using simple SQL queries, however a multi-criteria decision making problems are often not answered by normal SQL queries. In order to solve these types of queries we may need to use co-operative query languages etc. However using additional query based system incurs extra cost. Moreover, if the criteria in a query are complementary to each other simple SQL queries are not capable of addressing this issue. A query in which multi-criteria decision making is required, often more than a single attribute of the relation is analyzed to fetch the desired result. In this context dominance analysis is performed to obtain a set of points (tuples) those are at least equally good in all the dimensions in compare to other points in the dataset. Skyline points are computed to find points which are not dominated (dominance analysis) by any other point in the system. A point is called 'skyline point' if and only if it is not dominated by any other points in the system. Computation of skyline requires comparison of each point to all the other points in the system which in turn increases complexity. The complexity may increase at exponential rate when the numbers of dimensions increase. This research work focuses on the reduction of computational complexity. It is incorporated here by selecting the most important dimension of the database and transfers the other entire dimension in that form. And finally ranks the points accordingly. © 2016 IEEE.