Computational geometry: an introduction
Computational geometry: an introduction
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 17th International Conference on Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Searching in Metric Spaces by Spatial Approximation
SPIRE '99 Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Efficient computation of the skyline cube
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Efficient reverse k-nearest neighbor search in arbitrary metric spaces
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Dynamic skyline queries in metric spaces
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Z-SKY: an efficient skyline query processing framework based on Z-order
The VLDB Journal — The International Journal on Very Large Data Bases
Dynamic skylines considering range queries
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Discovering the most potential stars in social networks with infra-skyline queries
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Skyline queries, front and back
ACM SIGMOD Record
Monochromatic and bichromatic mutual skyline queries
Expert Systems with Applications: An International Journal
On efficient reverse skyline query processing
Expert Systems with Applications: An International Journal
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Given a set of n query points in a general metric space, a metric-space skyline (MSS) query asks what are the closest points to all these query points in the database. Here, consider for any point p, if there are no other points in the database which have less or equal distance to all the query points, then p is denoted as one of the closest points to the query points. This problem is a direct generalization of the recently proposed spatial-skyline query problem, where all the points are located in two or three dimensional Euclidean space. It is also closely related with the nearest neighbor (NN) query, the range query and the common skyline query problem. In this paper, we have developed new algorithms to aggressively prune non-skyline points from the search space. We also contribute two new optimization techniques to reduce the number of distance computations and dominance tests. Our experimental evaluation has shown the effectiveness and efficiency of our approach.