Comparative Analysis of Skyline Query Execution using Imputation Techniques on Partially Complete Data

. In this era, the Database community depends on preference queries to satisfy user needs according to their given preferences. Skyline query is one of the preference-based queries. The skyline proceeds with contradictory preferences given by the user. Skyline query derived from the maximum vector problem which deals with Pareto dominance. Skyline query always leads to promising results in the complete data environment. Due to the dynamic data setup, this leads to unknown values or noisy data in the database. This type of data leads partially complete data environment and this affects the performance of skyline queries. This paper gives an analysis of complete and partially complete data using skyline queries with imputation techniques. Two different imputation techniques are used namely Random forest and Amelia to execute the Skyline query on partially complete data. The experimental study gives the solemnity of partially complete data using the skyline query and its influence on the result of the query.