Computing Skyline Query on Incomplete Data

Skyline queries have been widely used in a variety of modern database applications. Multicriteria decision-making systems, decision support systems, and recommender systems are among those on the list. As skyline algorithms have so many advantages and may be used in so many various data contexts, they have previously been proposed in a variety of data situations. The scenario of having complete data is not valid in this digital era with a huge amount of data with a missing value. In practical applications, there is a direct dependence between data items and their incompleteness. Besides, dealing with incomplete data may arise problems like losing transitivity and cyclic dominance. With a view to handling these problems, we have proposed a new algorithm that deals with the incompleteness of each data and tries to predict the best data items. We have introduced the concept of the weighting factor. A new dataset has been made comprising rating of famous tourist places in Bangladesh to test the algorithm. Besides, the efficiency has been compared with existing datasets, and our algorithm has shown more practical output than others.