Applications of random sampling in computational geometry, II
Discrete & Computational Geometry - Selected papers from the fourth ACM symposium on computational geometry, Univ. of Illinois, Urbana-Champaign, June 6 8, 1988
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Small-dimensional linear programming and convex hulls made easy
Discrete & Computational Geometry
An optimal convex hull algorithm and new results on cuttings (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
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
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
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
Ranking uncertain sky: The probabilistic top-k skyline operator
Information Systems
MSSQ: manhattan spatial skyline queries
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Optimized skyline queries on road networks using nearest neighbors
Personal and Ubiquitous Computing
Topological operators: a relaxed query processing approach
Geoinformatica
Computation of non-dominated points using compact voronoi diagrams
WALCOM'10 Proceedings of the 4th international conference on Algorithms and Computation
Combining top-k query in road networks
WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
General spatial skyline operator
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
The Farthest Spatial Skyline Queries
Information Systems
Efficient general spatial skyline computation
World Wide Web
Efficient algorithms for spatial skyline query with uncertainty
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
MSSQ: Manhattan Spatial Skyline Queries
Information Systems
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As more data-intensive applications emerge, advanced retrieval semantics, such as ranking and skylines, have attracted attention. Geographic information systems are such an application with massive spatial data. Our goal is to efficiently support skyline queries over massive spatial data. To achieve this goal, we first observe that the best known algorithm VS 2, despite its claim, may fail to deliver correct results. In contrast, we present a simple and efficient algorithm that computes the correct results. To validate the effectiveness and efficiency of our algorithm, we provide an extensive empirical comparison of our algorithm and VS 2 in several aspects.