SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Proceedings of the 17th International Conference on Data Engineering
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
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Maximal vector computation in large data sets
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
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
Processing spatial skyline queries in both vector spaces and spatial network databases
ACM Transactions on Database Systems (TODS)
Spatial Skyline Queries: An Efficient Geometric Algorithm
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Spatial skyline queries: exact and approximation algorithms
Geoinformatica
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
In-Route skyline querying for location-based services
W2GIS'04 Proceedings of the 4th international conference on Web and Wireless Geographical Information Systems
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Skyline queries have gained attention lately for supporting effective retrieval over massive spatial data. While efficient algorithms have been studied for spatial skyline queries using the Euclidean distance, these algorithms are (1) still quite computationally intensive and (2) unaware of the road constraints. Our goal is to develop a more efficient algorithm for L"1 distance, also known as Manhattan distance, which closely reflects road network distance for metro areas. We present a simple and efficient algorithm which, given a set P of data points and a set Q of query points in the plane, returns the set of spatial skyline points in just O(|P|log|P|) time, assuming that |Q|@?|P|. This is significantly lower in complexity than the best known method. In addition to efficiency and applicability, our algorithm has another desirable property of independent computation and extensibility to L"~ norm distance, which naturally invites parallelism and widens applicability. Our extensive empirical results suggest that our algorithm outperforms the state-of-the-art approaches by orders of magnitude. We also present efficient algorithms that report the changes of the skyline points when single or multiple query points move along the x- or y-axis.