A database model, which is capable of handling uncertain or imprecise queries, always seeks the attention of researchers as the real-world queries are very often uncertain or imprecise in nature. Fuzzy set, introduced by Zadeh, was the first mathematical framework which dealt with uncertainty. Several theories were developed subsequently to cope up with uncertainty. Neutrosophic set proposed by Smarandache is one such framework dealing with imprecise information. To process uncertain queries in a database based upon such frameworks, similarity measures plays an important role. It is one of the factors that helps in choosing the right alternative from a database. Most of the similarity measures are based upon existing distance measures. In this work, a new similarity measure proposed by Sarkar and Ghosh in 2024 for neutrosophic sets is deployed to choose the most appropriate alternative from a database with multiple attributes. It is observed that the measure performed perfectly in fetching the closest data when multiple uncertain queries were processed, even for a very large data set. Hence, it may be concluded that in future the measure will be effective in multiple attribute decision making problems.