Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 17th International Conference on Data Engineering
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Continuous k-dominant skyline computation on multidimensional data streams
Proceedings of the 2008 ACM symposium on Applied computing
On Skylining with Flexible Dominance Relation
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On finding skylines in external memory
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Skyline queries are useful in many applications such as multi-criteria decision making, data mining, and user preference queries. A skyline query returns a set of interesting data objects that are not dominated in all dimensions by any other objects. For a high-dimensional database, sometimes it returns too many data objects to analyze intensively. To reduce the number of returned objects and to find more important and meaningful objects, we consider a problem of k-dominant skyline queries. Given an n-dimensional database, an object p is said to k-dominates another object q if there are $(\textbf{k} {\leq} \textbf{n})$ dimensions in which p is better than or equal to q. A k-dominant skyline object is an object that is not k-dominated by any other objects. In contrast, conventional skyline objects are n-dominant objects. We propose an efficient method for computing k-dominant skyline queries. Intensive performance study using real and synthetic datasets demonstrated that our method is efficient and scalable.