Skyline queries have recently attracted considerable attention for their ability to return data points from a given dataset that are not dominated by any other points. This study extends the concept of skyline queries in the development of a σ-neighborhood skyline query (σ-N skyline query). In contrast to previous methods, the σ-N skyline query finds skyline points and points that are similar, i.e., close to the skyline points. The σ-N skyline points are useful to the user if a skyline point, compared to its σ-N skyline point, is less competitive. In applications such as decision making, market analysis, and business planning, σ-N skyline can provide more flexible answers. This study defines this problem and proposes a new index tree and efficient algorithms to resolve the problem. We conducted a set of simulations to demonstrate the effectiveness and efficiency of the proposed algorithm.
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