Skyline computation over multiple points and dimensions

Skyline is a technique in database management system for multi-criterion decision making based on dominance analysis. Skyline overcomes the limitation of relational databases by handling the criteria that are inversely proportional to each other. Traditional skyline operation is conceptualized over two dimensions only, and it finds out single interesting point. In this paper we extend the capability of skyline to work with multiple dimensions and to search the multiple interesting points from the given search space. The work furthermore ranks skyline points with respect to the multiple interesting points. However, we restrict the computational complexity within a fixed upper bound. Skyline is commonly applied on tourism industries, and we consider two different case studies from this domain and execute the proposed methodology over the real-life data. Comparative study is given based on different parameters, and statistical analysis is also performed to illustrate the efficacy of the proposed method over the existing methods.