During the past two decades, the database community has witnessed an increase in research interest in skyline queries. These queries report interesting objects, also called skylines, to users based on their preferences. However, identifying skylines from a collection of input data streams that are continuously generated is challenging. Moreover, the high rate of recurring objects in the stream leads to redundant dominance analyses that are unnecessary, adding to undesirable execution time. This paper evaluates the performance of three skyline algorithms in dealing with redundant dominance analyses, namely: conventional skyline algorithm (CSA) that does not rely on any prior dominance analyses in deriving the skylines of a given dataset, SA + FDA&RL that employs the FDA&RL which contains prior dominance analyses that have been filtered through Apriori algorithm, and SA + FDA&RL + HT that retrieves the dominance analyses from buckets of the FDA&RL which are formed through the hashing technique. Several analyses have been designed and the results show that SA + FDA&RL + HT has significantly reduced the execution time as compared to CSA and SA + FDA&RL. The findings from this study will assist in accelerating decision-making and improving responsiveness of business operations.