BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Bro: a system for detecting network intruders in real-time
Computer Networks: The International Journal of Computer and Telecommunications Networking
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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
Efficient computation of the skyline cube
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
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Skyline queries, which return the objects that are better than or equal in all dimensions and better in at least one dimension, are useful in many decision making and real-time monitor applications. With the number of dimensions increasing and continuous large volume data arriving, mining the thin skylines over data stream under control of losing quality is a more meaningful problem. In this paper, firstly, we propose a novel concept, called thin skyline, which uses a skyline object that represents its nearby skyline neighbors within Ɛ-distance (acceptable difference). Then, two algorithms are developed which prunes the skyline objects within the acceptable difference and adopts correlation coefficient to adjust adaptively thin skyline query quality. Furthermore, our experimental performance study shows that the proposed methods are both efficient and effective.