Efficient algorithms for finding the most desirable skyline objects

The skyline query is a powerful tool for multi-criteria decision making. However, it may return too many skyline objects to offer any meaningful insight. In this paper, we introduce a new operator, namely, the most desirable skyline object (MDSO) query, to identify manageable size of truly interesting skyline objects. Given a multi-dimensional object set and an integer k, a MDSO query returns the most preferable k skyline objects, based on the newly defined ranking criterion that considers, for each skyline object s, the number of the objects dominated by s and their accumulated (potential) weights. We devise the ranking criterion, formalize the MDSO query, and propose three algorithms for processing MDSO queries. In addition, we extend our methods to tackle the constrained MDSO (CMDSO) query. Extensive experimental results on both real and synthetic datasets show that our presented ranking criterion is significant, and our proposed algorithms are efficient and scalable.