Communications of the ACM
The LSD tree: spatial access to multidimensional and non-point objects
VLDB '89 Proceedings of the 15th international conference on Very large data bases
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
Multidimensional access methods
ACM Computing Surveys (CSUR)
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Proceedings of the ACM 2000 conference on Java Grande
Multidimensional binary search trees used for associative searching
Communications of the ACM
ACM Computing Surveys (CSUR)
Proceedings of the 17th International Conference on Data Engineering
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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
Algorithms and analyses for maximal vector computation
The VLDB Journal — The International Journal 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
Parallel programming: can we PLEASE get it right this time?
Proceedings of the 45th annual Design Automation Conference
Parallel skyline computation on multicore architectures
Information Systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Proceedings of the 15th International Conference on Database Theory
Parallel Computation of Skyline Queries
FCCM '13 Proceedings of the 2013 IEEE 21st Annual International Symposium on Field-Programmable Custom Computing Machines
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Given a sequential input connection, we tackle parallel skyline computation of the read data by means of a spatial tree structure for indexing fine-grained feature vectors. For this purpose, multiple local split decision trees are simultaneously filled before the actual computation starts. We exploit the special tree structure to clip parts of the tree without depth-first search. The split of the data allows us to do this step in a divide and conquer manner. With this schedule we seek to provide an algorithm robust against the "dimension curse" and different data distributions.