Continuous <i>k</i>-Dominant Skyline Computation by Using Divide and Conquer Strategy

Skyline objects in a database are objects that are not dominated by any other object in the database. Skyline queries retrieve a set of skyline objects so that the user can choose promising objects from them and make further inquiries. Therefore, such skyline queries are important for several database applications. However, a skyline query often retrieves too many objects to analyze intensively especially for high-dimensional dataset. Recently, k-dominant skyline queries have been introduced, which can reduce the number of retrieved objects by relaxing the definition of the dominance. On the other hand, the maintenance of k-dominant skyline objects under continuous updates is much more difficult compared to conventional skyline objects. This paper addresses the problem of efficient maintenance of k-dominant skyline objects of frequently updated database. We propose an algorithm based on divide and conquer strategy for maintaining k-dominant skyline objects. Intensive experiments using real and synthetic datasets demonstrated that our method is efficient and scalable.

Skyline objects in a database are objects that are not dominated by any other object in the database. Skyline queries retrieve a set of skyline objects so that the user can choose promising objects from them and make further inquiries. Therefore, such skyline queries are important for several database applications. However, a skyline query often retrieves too many objects to analyze intensively especially for high-dimensional dataset. Recently, k-dominant skyline queries have been introduced, which can reduce the number of retrieved objects by relaxing the definition of the dominance. On the other hand, the maintenance of k-dominant skyline objects under continuous updates is much more difficult compared to conventional skyline objects. This paper addresses the problem of efficient maintenance of k-dominant skyline objects of frequently updated database. We propose an algorithm based on divide and conquer strategy for maintaining k-dominant skyline objects. Intensive experiments using real and synthetic datasets demonstrated that our method is efficient and scalable.