With the increase of data volume, advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline queries help us to filter unnecessary information efficiently and provide us clues for various decision making tasks.
Most of the existing skyline algorithms cannot preserve individual's privacy and are not well suited for data with outliers and frequently updated data. Considering these issues, earlier we have proposed skyline sets queries from databases where all dimensions are available for all data items and considered an efficient algorithm for computing convex skyline sets. In this paper, we use that idea for skyline sets queries for incomplete data and propose a method, namely, RBSSQ. RBSSQ method uses a replacement-based approach and is applicable to the databases having any number of missing dimensions in the database objects. We have conducted several experiments in terms of computational cost and found that our proposed method can efficiently compute skyline sets from data items with missing values.