Proceedings of the 17th International Conference on Data Engineering
Mining thick skylines over large databases
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Stabbing the Sky: Efficient Skyline Computation over Sliding Windows
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Catching the best views of skyline: a semantic approach based on decisive subspaces
VLDB '05 Proceedings of the 31st international conference on Very large data bases
SUBSKY: Efficient Computation of Skylines in Subspaces
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Refreshing the sky: the compressed skycube with efficient support for frequent updates
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
K-Dominant Skyline Computation by Using Sort-Filtering Method
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Continuous Processing of Preference Queries in Data Streams
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
On finding skylines in external memory
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Parallel skyline queries over uncertain data streams in cloud computing environments
International Journal of Web and Grid Services
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Skyline queries are important due to their usefulness in many application domains. However, by increasing the number of attributes, the probability that a tuple dominates another one is reduced significantly. To attack this problem, k-dominant skylines have been proposed, relaxing the definition of domination. In this paper, we study the problem of continuous monitoring of k-dominant skylines, where multiple queries are running concurrently. The proposed method divides the space in pairs of attributes. For each pair, we compute skyline tuples and we exploit them to eliminate candidates tuples of the queries and we combine the partial results. The proposed scheme uses only simple domination checks and it is applicable to the streaming case as well as to ad-hoc insertions and deletions. Experiments, based on different data distributions, show the efficiency of the proposed scheme in comparison to existing methods.